数值化赋能高校思想课教学

高校思政课线上线下混合式教学是推动思想政治理论课改革创新的举措之一，当前高校思政课混合式教学存在线上教学资源不足、教学设计不够合理和教学评价体系不健全等问题。通过利用数字技术，数字赋能高校思政课混合式教学，构建形式多样的数字化线上教学资源、搭建虚拟仿真实践教学线上大平台、设计“教师主导、学生主体”的教学设计和完善思政课线上线下全过程数字化考核评价体系等优化策略，进一步提高高校思政课的思想性、理论性、针对性和亲和力，增强大学生思政课获得感，为推动思政课教学改革创新提供新思路。The blended teaching approach, combining online and offline elements, is one of the measures to promote reform and innovation in ideological and political theory courses in universities. However, there are currently issues in this blended teaching approach, such as insufficient online teaching resources, inadequate teaching design, and an underdeveloped teaching evaluation system. By leveraging digital technology and empowering the blended teaching of ideological and political courses in universities with digitalization, we can construct diverse digital online teaching resources, establish a large virtual simulation practice teaching platform, design teaching designs that prioritize teacher guidance and student engagement, and refine the digital assessment and evaluation system for the entire process of ideological and political courses both online and offline. These optimization strategies aim to further enhance the ideological and theoretical depth, relevance, and accessibility of ideological and political courses in universities. This, in turn, strengthens the sense of achievement among university students in these courses and provides new insights for promoting reform and innovation in ideological and political course teaching.

Abstract In this paper, a large amount of data related to the teaching of ideological and political courses is collected using information technology and preprocessed in the four dimensions of data cleaning, missing value processing, sample labeling, and expert sample data. Aiming at the problem of underfitting of traditional neural network algorithm in the evaluation of digital teaching effect of ideological and political courses, the RBF neural network is improved and optimized by combining radial basis function and radial basis interpolation, and a teaching evaluation model based on the enhanced RBF network is constructed. The combination of statistical and simulation analysis is used to analyze the learning behavior of digitally empowered ideological and political courses. The results show that among the five types of teaching activities, participation in after-class discussion (-1.6443) performs better compared to the other four types of teaching activities (-1.7541, -1.6815, 1.7331, -1.8265), indicating that the neural network algorithm based on the Improved RBF accurately reflects the learning behavior of the group in the teaching of Digital Empowerment Ideology and Politics Course. This study realizes the scientific, modern and intelligent development of digitally empowered ideological and political course teaching. It promotes digital ideological and political course teaching to be more and more scientific and philosophical.

Under the new situation, the network with a unique open, virtual, interactive features, makes the students participate in the higher degree and pays more attention to the Ideological and Political Theory Course, it can also optimizes and enhances the teaching mode. In the teaching practice of the curriculum "the Outline of Chinese Modern and Contemporary History" in Guilin university of Aerospace Technology, we make full use of the Internet, innovative teaching carrier, and create integrated digital classroom for "PC +Mobile Phone + WeChat" which forms new teaching ecology.

The development of intelligent digital education poses new challenge to the ideological and political (abbreviated as IAP for short in the paper) work of college students. And it has brought fundamental changes to college students' learning and lifestyle. Under such circumstances, colleges and universities began to establish network platforms for IAP education. At present, the platform not only needs to meet the needs of ideology, modernization of education, personal and other aspects, but also has problems such as insufficient content and insufficient business personnel. It is necessary to adhere to the new development concept as the guide, strengthen the construction of online and offline platforms, explore the depth of platform contents, and continuously perfect the development of IAP education platform in colleges and universities under the new media environment. Under intelligent digital education, IAP work in colleges and universities must be guided by the new development concept, actively expand the connotation of online and offline platforms, continuously improve the construction of IAP positions in colleges and universities and promote the innovative development of IAP work. The college IAP education platform has strong communication power, considerable practical effects and a wide range of student groups, and can produce multi-dimensional educational effects such as network, practice, culture, management, service, and organization. The article analyzed the current network environment of college students' IAP education, and conducted an in-depth analysis of the main platform, internal auxiliary platform, off-campus auxiliary platform and its own problems of the current IAP education network platform in colleges and universities. This paper conducted a questionnaire survey and finds that more than 80% of the students are in favor of network optimization on the IAP education network platform.

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The study explores how educational digitalization enables the precise development of ideological and political education in colleges and universities. Digital transformation enables colleges and universities to accurately define educational objectives, content strategies, effect evaluation, and process management, and realize the precision and intelligence of ideological and political education. The application of big data technology enhances the data-oriented thinking of teachers and students, promotes the accurate application of data, and improves the efficiency of ideological and political education. The research also prospected a new vision of the digital construction of ideological and political courses and clarified the theoretical and practical path of the implementation and evaluation mode of ideological and political courses under digital empowerment. Education digitalization enables precise ideological and political education, which is a key way to promote the innovative development of ideological and political education in colleges and universities and will strongly support the improvement of the overall quality of higher education and the training of excellent talents.

Digital technology has opened up new prospects for intelligent teaching in ideological and political theory courses in universities. Intelligent teaching, an innovative application of digital technology in the field of teaching, refers to a new teaching model that relies on digital technologies such as artificial intelligence, centers around serving teachers and students, and enhances the quality of teaching and the learning experience of students through intelligent teaching environments, platforms, machines, and methods. It represents an important trend in the reform and innovation of ideological and political courses in universities. The intelligent teaching of ideological and political courses in universities is based on machine learning, natural language processing, and human-computer interaction technologies. The teaching advantages and potential of digital technology, coupled with the need for students' intelligent adaptive learning, form the realistic foundation for empowering intelligent teaching in ideological and political courses in universities. Digital technology has created intelligent teaching environments, platforms, and machines for ideological and political courses in universities. To further promote intelligent teaching in ideological and political courses in universities, it is also necessary to enhance the technical literacy of teachers, improve the performance of digital teaching technologies, and strengthen the application of intelligent technologies in teaching scenarios.

With the advancement of digital technology, new means and methods are being offered in education to enhance teaching-learning. The use of technology in educational interventions has a positive impact on the students’ functional, cognitive, and psychomotor skills. This study aims to improve the teaching mode of ideological and political education (IPE). The research work intends to enhance enthusiasm of college students for ideological and political course (IPC). Based on 5G technology, the rising virtual reality (VR) technology is proposed in this study to improve IPC in colleges and universities. To begin with, the background of IPE and VR technology is introduced. Next, the current college students’ attitudes toward IPE and VR technology are investigated by a questionnaire survey. Finally, based on the red hero culture, the VR system for IPE is designed. Results of the evaluation show that the students who have been the members of the Communist Party have the highest degree of interest for IPC in colleges and universities. Endorsing the fact, about 29.7% college students and 26.81% university students who were members of the communist youth league favored for IPC, while only 14.29% of the masses showed interest for IPC. As a whole, 25.2% of all the students participated in the survey showed their interest for IPC. One of the main reasons behind the results is the teaching mode and means that attract students for IPC. At present, 84.11% of the ideological and political teachers give a lecture of IPC via PPT, while 67.29% of the students prefer the Internet platform as a means of IPC. About 73.4% of the students know VR and are interested in the application of VR to IPC. They believe that VR can help them understand the knowledge of IPC and has broad prospects in improving the teaching quality of IPE. The designed VR system for IPE which is used in colleges and universities has four modules, namely, the preamble module of graphic history, the hero wall module, the hero art sculpture module, and the scene experience module. This system can help realize the objective of IPE in colleges and universities and provides a reference for the application of VR in enhancing IPE.

In the new era and new journey, the construction of ideological and political courses is facing new situations and new tasks, and there must be new atmosphere and new achievements. Digital empowerment of the modernization of ideological and political courses in colleges and universities is the need to promote the party's innovative theories to be deeply rooted in the hearts of the people, the need to better serve the construction of a strong country in education, and the need to promote the development of Chinese-style modernization. From the perspective of Marxist philosophy, this paper examines the value implication of digitally empowering the modernization of ideological and political courses in colleges and universities. Second, from special to general: the digitalization of ideological and political courses promotes the modernization of education; The third is from the object to the subject: the modernization of ideological and political courses promotes the modernization of teachers and students. Explain its value implication from three dimensions, enrich the connotation of the times of ideological and political courses, continuously improve the pertinence and attractiveness of ideological and political courses, and strive to cultivate more newcomers of the era who can reassure the party, love and dedicate themselves, and take on the important task of national rejuvenation.

With the comprehensive promotion of the information education 2.0 action plan, digital technology has become the key content to promote the precise development of modern education. In view of the practical problems faced in the ideological and political construction of physical education courses in colleges and universities at the present stage, the university should comprehensively promote the application of digital technology, so as to build a precise ideological and political curriculum system, and implement the fundamental task of cultivating moral education by virtue through the concept of "sports people", so as to create a new platform paradigm for the ideological and political construction of physical education courses. In this context, this paper analyzes the application value and application dilemma of digital technology in the precise ideological and political construction of physical education, and puts forward the basic strategy of digital technology to empower the precise ideological and political course construction of physical education in universities.

With the deepening of digital transformation, higher education has undergone profound changes in teaching models and content delivery. This paper explores how digital transformation can empower the implementation of university ideological and political courses, aiming to enhance teaching effectiveness and student engagement through the application of digital technologies. First, it analyzes the concept of digital transformation and its impact on education, examines the current status and challenges of ideological and political courses in universities, and argues for the necessity of digital empowerment. Next, from multiple perspectives, including the construction of digital platforms, the design of teaching content, the enhancement of teacher competencies, the innovation of student interaction mechanisms, and assessment and feedback systems, it systematically elaborates the application pathways of digital transformation in these courses. Drawing on practical case studies from domestic universities, the paper discusses actual effects and issues encountered. Finally, it proposes measures to strengthen policy support, teacher training, and university–industry cooperation during implementation, and suggests directions for future research. This study aims to provide both theoretical support and practical guidance for the innovation and development of university ideological and political courses.

Artificial intelligence model combined with data mining technology can mine useful data from college ideological and political education management, and conduct process evaluation and teaching management. Therefore, based on the superiority of data mining technology and artificial intelligence system, this paper improves the traditional algorithm and constructs a university ideological and political education management model based on big data artificial intelligence. Moreover, this study uses a local sensitive hash function to generate representative point sets and uses the generated representative point sets for clustering operations. In order to verify the performance of the algorithm model, a control experiment is designed to compare the algorithm of this paper with traditional data mining methods. It can be seen from the research results that the algorithm model constructed in this paper has good performance and can be applied to practice.

The era of big data has had a more accurate, timely, extensive and lasting impact on the way of thinking and behavior of college students, which puts forward the reform requirements of "sense of the times" and "attraction" for ideological and political education in colleges and universities. However, the dilemmas in the process of combining the two are objective, such as the problems of the traditional subject-object relationship between the ideological and political educators and college students and the top-level design of school administration. Therefore, a two-way transformation combining top-down and bottom-up is the era requirement for traditional ideological and political education in the era of big data.

Based on the new situation and requirements of big data era, this paper aims at the relations of schools, teachers and students, revolves the socialist core values of the Ideological Education lifeline, puts forward the important principles of the innovation of the ideological and political education of college students, thinks of specific methods and measures for further work of the big data era was proposed to ideological and political education.

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Abstract The Ideological instruction work is related to the future and destiny of socialism with Chinese characteristics. The arrival of big data era has brought opportunities and challenges to this arduous and glorious cause. The combination of science and technology represented by big data, Internet, cloud computing and Internet of things will inevitably lead to a new round of innovation and development. Through questionnaire survey, in-depth interview and literature analysis, this paper finds out the main problems of college students’ network Ideological instruction problems in the big data era. Through the questionnaire survey, we can see that the proportion of students who surf the Internet 4-6 hours and more than 6 hours a day is more than 47%, and more than 77% of the attention content in the school portal is academic achievement, combined with the extended interview with Ideological instruction workers in colleges and universities.

Abstract With the development of information technology and the growth of new media, the big data perspective is coming. A detailed introduction to the big data is performed in this paper, and the faced by the ideological and political education in colleges and universities are analyzed from the big data perspective. Combined with the above analysis, the specific reform measures are proposed to guide the teaching practice.

The ideological and political education is facing new and greater challenges in the large data environment. In the current environment, the full use of information technology to carry out ideological and political education work, is the effective strategy to adapt to the big data environment. This paper mainly introduces the new situation of College Students' Ideological and political education, and according to the characteristics of College Students' Ideological and political education work way of innovative research.

The construction of the index system is incomplete in the accuracy evaluation of ideological and political work in Colleges and universities, which leads to the poor effect of the accuracy evaluation of ideological and political work in Colleges and universities. Therefore, this paper proposes to introduce artificial intelligence big data technology to improve the accuracy of ideological and political work in Colleges and universities. Analyze and improve the starring participants in ideological and political work in Colleges and universities, determine the basic principles to improve the accuracy of ideological and political work in Colleges and universities, determine the importance indicators of ideological and political teachers’ teaching, students’ classroom learning, after-school practice, and school ideological and political work, and divide them into primary indicators and secondary indicators. The naive Bayesian model is used to decompose the accuracy indicators of ideological and political work in Colleges and universities, build the accuracy evaluation model of ideological and political work in Colleges and universities, and realize the research on improving the accuracy of ideological and political work in Colleges and universities. The experimental results show that this method can effectively improve the integrity of the accuracy evaluation index of ideological and political work in Colleges and universities and improve the accuracy evaluation effect of ideological and political work in Colleges and universities.

The construction of a correct worldview, outlook on life, and values for students is linked to the development and breakthrough in the management of ideological as well as political education of students. At the same time, college students must be encouraged to follow well-rounded education and struggle to be well-prepared for the challenges of the new era. In order to raise students’ understanding of the critical role that political and ideological education plays in their academic success, it is authoritative that efforts to integrate these two spheres of learning be extended and new encounters made. That’s what prompted this study, which is focused on assessing college students’ level of ideological and political education administration, and it uses a mixture of big data technologies as well as artificial intelligence (AI) to do it. The accuracy of the traditional ideological as well as political education management quality assessment algorithm is not high, feature information extracted by the single-scale neural network (NN) is not rich enough, and the multiscale convolutional network (CN) fusion cannot consider the different values and importance for each scale. In this paper, the convolution kernel of the two-dimensional CN is changed to a one-dimensional convolution kernel, and the multiscale feature fusion CN model MCNN is first designed. The model is optimized and improved, the attention mechanism is integrated, and the MACNN model for the management evaluation of ideological as well as political education is proposed. Besides, this work organizes the network model in a wireless network environment that users can contact and operate at any time.

With the rapid development of science and technology and the structure of social transformation, the college students’ ideological and political work faces the new challenge and has the opportunities for strengthening and innovation. In this paper, innovation of ideological and political work in Colleges and universities in the era of big data has been proposed, the research point of view have successfully solved innovative working methods. Firstly following the spirit of the times, strengthen the leading role of leading thought. Secondly grasping the ideas and means of innovation spirit, innovation based essence. Finally playing new situation of ideology made the fundamental task and the exact implementation of the historical mission of moral education.

Analyzing, judging and sorting out data through big data technology helps us grasp the prospects and laws of the development of things. Especially for the future development direction of things, more accurate calculations can be made, and scientific and reasonable measures can be taken to promote the development of things according to the calculation results. At present, in the process of ideological and political education in my country's colleges and universities, the teaching methods are mainly traditional teaching methods. In classroom teaching, teachers instill the corresponding curriculum theory, the teaching method is relatively simple, and the curriculum content is boring, which seriously affects the teaching efficiency and teaching quality of college ideological and political classrooms. The use of big data technology to build a curriculum network system that is more suitable for students' learning needs. Besides, the use of some simple extracurricular activities to assist students' education and teaching can mobilize students' enthusiasm and enthusiasm for participating in ideological and political education in colleges and universities, which is conducive to giving full play to ideological and political education in colleges and universities positive effect. Therefore, we need to pay attention to the positive role of big data technology in college ideological and political education, and use big data technology to strengthen college ideological and political education.

To make full use of college English classroom to perform ideological education in the era of big data, this paper provides a scheme of using analytic hierarchy process(AHP) to evaluate the quality of ideological and political teaching. We construct the evaluation index system of college English ideological and political education, and use AHP method to determine the weight of each index. Then we introduce the method and main process of determining the weight of evaluation index combined with empirical analysis, and test the effectiveness of the model. Finally, the three-tier technology architecture of display/logic/data processing separation and JSP + JavaBean technology are used to realize the development of college English ideological and political education quality evaluation system. Through the system test and analysis of the coordination rate computer, the objective results of teacher evaluation are provided with the visual interface, which proves that the teaching quality evaluation model has good calculation accuracy, and provides a new scientific way for English teachers' teaching quality evaluation.

To meet the large data analysis needs of ideological and political education in colleges, this paper designs an assistant expert analysis system based on Hadoop. It integrates the functions of data acquisition, data storage, data analysis and data visualization, and provides a friendly human-computer interaction interface to shield the details of large data analysis algorithm, to help teaching staff use large data analysis technology simply and efficiently for high concurrency and rapid query. We classify and design the basic operation model based on MapReduce for users to configure complex data processing logic freely. The system can also add reusable and extensible computing and analysis module to the platform according to the field experience, to expand the data analysis and processing capabilities of the system, which facilitates users to explore iterative and incremental data.

The ideological and political education is an important part of the education system in colleges and universities, which has an extremely important educational significance for the improvement of college students' ideological and moral cultivation and scientific culture and the formation of the concept of life values. Under the background of big data era, ideological and political education in colleges and universities should continue to cater to the current development trend of the times and innovate and improve their own educational methods in order to better improve the level of ideological education and the quality of education. This paper expounds the value of innovative ideological and political education methods in colleges and universities in the era of big data, analyzes the impact of the era of big data on the innovation of ideological and political education methods in colleges and universities.

We need to make a new summary and explanation on the new circumstances and new issues that data development brought to University's ideological and political education, which is not only a new mission for ideological and political educators in the era, but also new opportunities for innovation and development of ideological and political education theory. From the perspective of pedagogy, sociology and other disciplines, this paper discusses and illustrates the basic elements content optimization, model building, etc. of ideological and political education in colleges and universities in the age of big data, , from an entirely new perspective.

Under the background of big data, the way and channels for people to obtain information have changed to some extent. The emergence of big data platforms not only expands the channels for people to obtain information, but also facilitates the way people obtain information and broadens people's horizons. The emergence of big data platforms brings new opportunities to the education industry, but also leads to greater new challenges for the development of the education industry. In the ideological and political education of colleges and universities, the application of big data platform can not only enrich the content of ideological and political education, but also reform the form of education, thereby improving the level of ideological and political education in colleges and universities. However, there are still many problems in the application of big data platform in education, which need to be paid attention to and improved in the process of application. From the background of big data, this paper conducts research on the importance of network ideological and political course education in colleges and universities, and tries to propose a path to optimize the quality of ideological and political education.

Abstract Human beings embrace the information technology era through their own wisdom, and the information age has brought indelible contributions to human beings in all fields. Based on the expectation that the computer big data will still work in the future, this paper puts forward some practical application directions for its further utilization and creative application in ideological and political education in colleges and universities through the analysis of many small aspect.

Big data deeply affects and changes the mode and approach of ideological and political education, which challenges both the subjects and the objects of ideological and political education. It threatens the authority of the subject position of ideological and political education in colleges and universities, diversifies the ideology of the object of ideological and political education, and complicates the environment of ideological and political education in colleges and universities. But at the same time, it also brings opportunities: it will promote the optimization of ideological and political education of college students based on big data, such as personalized teaching, fined service and scientific management. Therefore, it is necessary to broaden the collection path of big data of ideological and political education, build a professional team of big data of ideological and political education in colleges and universities, improve the level of teacher information, and improve the teaching mode of ideological and political theory course in colleges and universities.

Abstract Do a good job in the way of college students’ network political innovation based on big data thinking, so that it can play a stronger advantage and energy in college education. Carrying out innovative exploration of college students’ curriculum education based on big data thinking can continuously deepen the theoretical research significance of Internet political education. It can also put forward suggestions for better practice of network ideological and political education in colleges and universities, which has the dual significance of theoretical construction and practical guidance. Therefore, the MCA-sampling model is designed in this paper. According to the calculation of the sampling model, the opportunity for online ideological and political data literacy is 11%, and the challenge is 89%. This is because any flaws at any level will bring a severe test to the calculation of the effectiveness of online political teaching methods for students, which greatly increases the difficulty of online political educators. Through the horizontal comparison, it can be seen that the acceptance theory focusing on “receiver-centered” is the most innovative. Its innovativeness is 83%. The probability of timely method innovation is as high as 89%. The most unstable aspect of innovation probability is the root cause. Its probability of innovation is at least 24%.

The arrival of the big data era is profoundly changing people's social structure and working methods, and the field of education is naturally not spared. Driven by a large amount of information and technology, the pace of educational change is becoming more and more rapid, and networking and intelligence have become the keywords of the new generation of education, especially in the aspect of network ideological and political education in colleges and universities, this change is more important. This paper will explore these new trends and challenges in depth, analyze their impact and significance on network ideological and political education in colleges and universities, and combine them with specific teaching cases to put forward corresponding solution strategies and directions, aiming to further promote the modernization of the cause of ideological and political education in China's colleges and universities.

Along with the era of big data are new opportunities and challenges for the ideological and political education in China. It is an important issue in institutions of higher education in China to aid ideological and political education in colleges and universities by making effective use of big data technology and thinking. The application of big data aids ideological and political education in colleges and universities by improving its relevance, empowering it with computing capability, and providing it with think tank support. At the same time, big data also plays an increasingly important role in and has a significant influence on the specific practice of ideological and political education in colleges and universities.

In the big data era, the ideological and political education in Colleges should not only adhere to the traditional education methods, but also follow the development of the network to carry out method innovation.

Through reference reading and comparative analysis, this paper conducts a research on the effects of ideological and political education of college students in the era of big data. Firstly, it introduces the basic concept of such education; Secondly, it reveals the incoming opportunities and challenges towards such education by analyzing features of the era. Finally, based on these features, it puts forward corresponding methods. In the era of big data, new ways can be used to receive information and combine advanced IT with online resources. Also, a new model of education can be created by establishing a platform of data technology and MOOCs (Massive Open Online Course) and a strong pipeline of talent can be built so as to make such education more effective.

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Universities are not only the front line of national ideological and political works but also the gathering place of national talent training, which is of far-reaching significance to the national strategic development, and it is vital to do a good job of ideological and political education in universities for the construction and development of the country. "The university has become a place where wireless network technology can be used. The paper examines the current state of mobile learning in the United States and abroad, explains the principles for developing a mobile ideological and political learning system, designs and studies the system's overall structure and functional modules, and shows how the system's application can replace traditional ideological and political learning methods, allowing students to improve their ideological and political abilities using mobile devices.

Big data on the Internet has brought another way of thinking to college students’ ideological and political research. Teachers in higher education can make full use of the ability of big data in acquiring, storing, managing, and analyzing to innovate ideological and political education work. University faculty can improve their careers, ensuring that university education and politics are scientifically sound and efficient. To explore the modernization of college student ideological and political research in the age of Internet big data, this research usually begins with data that attracts college students and explores college student ideas and culture in a variety of ways. I received the information. Therefore, in the current Internet big data era, the innovation of the current college students’ ideological and political education work is deeply analyzed. The current traditional single “indoctrination” theory education can hardly continue to play a strong role. Therefore, starting from the actual requirements of college students, this paper puts forward a new point of view on the choice of educational methods. From the current survey, it is found that college students are still more inclined to choose the method of classroom teaching. Below the significant level of 0.05, the tolerance is greater than 0.1 and the variance expansion factor VIF is both. It is the smallest in the vicinity of 1. Therefore, the more the college students prefer classroom teaching and the way of receiving information from the mass media, the better the effect of traditional ideological and political education and network.

Big data is the product of the development of the times, which affects all aspects of social development and reorganizes the ideological and political network system of universities. The development of big data brings new opportunities and challenges to the NIPE (network ideological and political education) system of universities. Therefore, universities should pay attention to the data in the ideological and political network system. The NIPEDM service platform was developed, which combines the existing Web-based PC and mobile development technology to find a new educational DM (data mining) path. Using classifiers to predict students’ future performance, a new NB_BPNN (Naive Bayes-BP neural network) model is proposed, which effectively combines the advantages of two existing models. The experimental results show that the new model has achieved good results in the field of learning evaluation.

In the context of big data, ideological and political education in colleges and universities has become particularly important. Teachers should actively introduce new ideas and methods of ideological and political education, so as to stimulate students’ interest in ideological and political learning as well as develop their ideological and political level [1]. In view of this, this study explores the construction of college network ideological and political education platform under the background of big data and proposes several strategies for reference.

In the current ideological and political teaching evaluation, due to many comprehensive evaluation standards, complex process, resource consumption and other reasons, the evaluation is insufficient or mere formality. Thus, this paper proposes a classroom evaluation assistant strategy based on big data technology. The scheme mainly uses a new multi class classification algorithm which takes the similarity direction between classes as the IBTSVM generation algorithm, reads the data from the web system, and trains it by SVM train. Through the method of cross validation, the parameter setting of SVM is obtained. Finally, the algorithm is applied to the evaluation of teachers’ teaching quality, and the samples are trained and verified by case analysis, which proves that the application of intelligent teaching evaluation method based on big data in teaching evaluation classification is feasible and effective.

The era of big data comes with the in-depth development of Internet information technology, which not only brings new innovations, new changes, new ways of life, thinking, and communication but also creates new perspectives and opportunities for the ideological and political education research of college students in resident universities. Based on the local strategy of Mianyang City, this study analyzes the challenges of the ideological and political education of college students in Mianyang City, explores the problems existing in the ideological and political education of college students in Mianyang City, and puts forward the strategies of big data technology to promote the sustainable development of ideological and political education of college students in Mianyang City.

Abstract In order to improve the ideological and political education of college students, this paper constructs a cloud technology education platform based on big data analysis and collection technology. Firstly, the collaborative filtering algorithm is used to filter and collect student information; secondly, the Pearson correlation formula is used for pre-processing, MAE (mean absolute error value) is used as an evaluation index; and finally, the data is combined with FCM (fuzzy C-mean) algorithm to filter and analyze as needed. Its application performance is examined to verify the rationality and practicality of the construction of the cloud technology education platform. The analysis results show that, compared with the three-tier neural network education platform and MOOC (Massive Open Online Course) education platform, the educational resource transmission time of the cloud technology education platform constructed in this paper is reduced by 1/3, and the load balancing deviation is reduced by 1/4. Among four different servers, the average error value of the platform constructed in this paper is as low as 0.35% and can reach as high as 0.65%. The detection rate reaches 97.64%, the false alarm rate is only 2.93%, and the leakage rate is only 1.13%. It can be seen that the cloud technology education platform constructed in this paper can improve the utilization rate of educational resources and realize the sharing of educational resources.

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With the continuous development and application of big data technology, the field of education is facing new challenges and opportunities. This paper takes the education of college students' ideological and political courses under the background of big data as the research object, explores the challenges faced by ideological and political course education in the context of information explosion and increasing personalized learning demands, and proposes corresponding strategies. Through literature review and challenge analysis, the paper finds the impact of the big data era on ideological and political course education, including challenges such as information overload and the diversity of students' personalized needs. In the section discussing strategies, the paper proposes specific measures such as optimizing course settings and content using big data technology, implementing personalized teaching, etc., to address these challenges. Finally, through the summary and outlook of the research, the paper emphasizes the theoretical and practical significance of solving the problems of college students' ideological and political course education in the era of big data.

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INTRODUCTION: To apply artificial intelligence technology to ideological and political education in colleges and universities, as well as to combine artificial intelligence technology with ideological and political education in colleges and universities, it is necessary that wireless networks have complete coverage. OBJECTIVES: How can ideological and political education in universities and colleges be merged with artificial intelligence? How can artificial intelligence be used to support progressive political education at the college level? Starting with these issues, this paper will focus on the development of ideological and political education in colleges and universities as its main research question and refer to artificial intelligence technology as a method of ideological and political education in colleges and universities supported by wireless networks. METHODS: By examining the characteristics of artificial intelligence and ideological education in colleges and universities, and combining the poor immediacy and weak timeliness of information dissemination, as well as the low degree of identification of student groups with the theoretical courses of ideological education in the field of ideological education under the view of artificial intelligence, to explore the path of innovation of ideological education, RESULTS: In order to adapt to the demands of AI technology and improve people's capacity to use AI learning software, we need start with educators and educated individuals. Additionally, the government should encourage the development of artificial intelligence technologies financially and technically. Finally, it examines how civic education in colleges and universities could be improved through the use of artificial intelligence. This would allow civic education to benefit from the advantages of AI technology. CONCLUSION: In order to support the positive and healthy development of political education courses in colleges and universities across the nation, this paper encourages the creation of concepts and methods for teaching politics in higher education institutions.

In the era of artificial intelligence, traditional teaching models can be replaced by intelligent teaching models, thereby effectively improving the efficiency of ideological and political teaching. This paper proposes a multi-frame sliding window double-threshold clutter map CFAR algorithm and analyzes its detection probability and false alarm probability formula. Moreover, the ideological and political teaching system based on artificial intelligence and improved machine learning is designed based on the B/S model. In addition, this article analyzes the practical teaching performance of the model combined with actual teaching and analyzes the teaching effect of the model in ideological and political education. Through experimental research, it can be seen that the performance of the experimental group is significantly higher than that of the control group, which verifies that the algorithm constructed in this article has a certain practical effect.

The current technological growth has made artificial intelligence techniques reach every industry. One such industry is education, which uses AI techniques such as machine learning for university courses. This article considers research on the ideological and political education in colleges and universities based on artificial intelligence and machine learning in a wireless network environment. Ideology is a set of ideas that guide politics and is also referred to as a common idea that a group of people shares. Machine learning is referred to as part of artificial intelligence that uses computer algorithms and statistical models to analyze the data. It can learn and adapt itself with the help of the data. The advantage of machine learning is that it does not require human intervention. The proposed system includes machine learning and artificial intelligence in ideology and political education. Machine learning techniques will revolutionize the educational sector with their efficiency and advantages. Thus, it is found that artificial intelligence and machine learning techniques provide enhanced teaching and learning in ideological and political education provided by colleges and universities. In this research binary search algorithm (BSA) is implemented to analyze the teaching and learning process. BSA is compared with the traditional classroom education and statistical learning methodology and observed that BSA has achieved 95% accuracy in the education training and testing and is higher than the other methods.

Today, with the development of the Internet, combining big data with ideological and political education in colleges and universities, building an education system through artificial intelligence technology is an indispensable link. The Agent-based intelligent teaching system is an interactive intelligent teaching system for higher education students. The Agent technology, expert system and.NET technology used in the intelligent system can make the education system more intelligent and closer to teaching practice. Starting from the current development status of the ideological and political education system, this article elaborates on the technology used in the education system, and finally builds an ideological and political education platform, hoping to bring certain reference value to the development and application of the ideological and political education system in colleges and universities.

With the rapid development of technologies such as big data analysis, machine learning, and cloud computing, artificial intelligence has made breakthrough progress in many fields. Artificial intelligence technology has also brought profound changes to higher education. Therefore, the ideological and political course in colleges and universities should integrate artificial intelligence technology into the teaching of ideological and political education and create an “intelligent ideological and political learning” to adapt to the goal of educational reform in the new era. This paper presents a research method of innovation ability of ideological and political course based on BP neural network and improved <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M2"> <a:mi>k</a:mi> </a:math> -means clustering algorithm. Firstly, this method obtains the objective index that can comprehensively measure the learning ability through BP neural network and acquires the evaluation score of learning ability. Then, SPSS software is utilized to test the correlation between the influencing factors and the index, harvesting the factors that significantly affect graduate students’ ideological and political learning ability. Finally, an improved <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M3"> <c:mi>k</c:mi> </c:math> -means clustering algorithm is designed, which clusters the graduate students according to the different characteristics of the survey objects and gives targeted suggestions for each class of individuals to improve their ideological and political learning ability. The experimental results indicate that the proposed method is feasible and effective. The research method of ideological and political course ability proposed in this paper is of great significance to the promotion of ideological and political education in the era of big data.

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In higher education teaching work, college students not only need to master the professional knowledge and professional skills they learn during their school study but also need to improve their self‐education and self‐cultivation and constantly improve their comprehensive ability of learning. At present, there are differences and relationships between the education of college students in civic and mental normal education, and how to play the role of the integration of the two educations has become a problem that needs to be considered in the current work of college students’ training. The integration between civic education and mental normal education can make up for the shortcomings of monolithic civic education and mental normal education work and also optimize the teaching methods between them from a certain perspective to achieve the development goals of complementing each other and not being independent of each other, so that students can understand more learning methods and contents that promote the normal development of their own minds and minds. In response to the problem that mind‐normal education cannot be automatically integrated into the teaching of university thought and political science courses, in the context of artificial intelligence, this paper proposes a multi‐channel‐based mind‐normal and ideological and political information fusion model. The model has two channels, BERT+CNN and BERT and BiLSTM‐Attention; firstly, the pretraining model BERT is used to obtain the word vector representation of the fused text context; then, the CNN network of channel one is used to enhance the ability of local feature extraction of the text, and the BiLSTM‐Attention model of channel two enhances the ability of long sequence text processing and is key. Finally, the fused features of channel 1 and channel 2 are classified using a softmax excitation function. To verify the effectiveness of the proposed model, experiments are conducted on public datasets to demonstrate the effectiveness of the proposed method.

With the rapid advancement of modern science and technology, computer technology has been greatly promoted, leading to the emergence of artificial intelligence. In the new era of ideological and moral education, higher education's ideological and political education is an inevitable path and a necessary guarantee for cultivating innovative talents with both moral integrity and professional competence. However, traditional teaching scenarios can no longer meet the needs of ideological and political education in the new era. The continuous progress in artificial intelligence technology provides new means and ideas for the "Grand Ideological and Political Course" teaching, including the cultivation goals of college ideological and political courses, the construction of teaching staff, and the reform of teaching models. In this context, college ideological and political courses need to fully leverage the advantages of technological empowerment, actively adapt to the changes of the times, and explore innovative teaching methods in the era of artificial intelligence, thereby enhancing the mission-driven ideological and moral education of the new era.

In recent years, with the vigorous development and application of Artificial Intelligence (AI), the application of AI in education is becoming more and more extensive. This study makes a theoretical analysis of AI-Intellectualized Information Technology (IT). Discrete Cosine Transform (DCT)-Based Speech Recognition (SR) and Genetic Algorithm (GA)-Based Image Recognition (IR) are used to analyze the College Ideological and Political Education (IAPE). The research findings prove that the advantages of integrating AI-intellectualized IT on College IAPE outweigh the disadvantages. The improvement of technological development, which accounts for 71.17% of undergraduate gains, is the most significant, and the smallest gain is technology coverage, which is 36.80%. Overall, 57.21% are interested in new technology, and the students' enthusiasm accounts for 30.77%. Most of the students focus on the innovation performance of technology, accounting for 75.92%. With an average influence of 89.04% on undergraduates, technology has the largest impact, followed by 85.78% on students with masters or higher degrees. The largest impact of diversified teaching methods for all students is 62.48%. This study provides some reference values for AI-intellectualized IT research and analysis, as well as students' IAPE.

MooTools (MOOC) is an emerging teaching mode in higher education in recent years, which is rapidly developing and expanding into the traditional teaching field. Based on the characteristics of MOOC platform, this paper explores the intelligent teaching of ideology and politics in the context of artificial intelligence and selects the Internet of Things (IoT) technology course as the research object to combine the MOOC hybrid teaching method with the teaching of IoT technology course. In this paper, a two‐level dynamic student model is proposed, which has the following characteristics: (1) comprehensive characteristics of learners are obtained through a combination of tracking and comprehensive evaluation, (2) secondary characteristics of learners are maintained to the instructor in an open and dynamic way, and (3) information of secondary characteristics is organized in the form of running accounts. Based on the two‐level dynamic student model, the generalized fuzzy integrated evaluation method is used to evaluate the secondary characteristics of the learners. Through this project, effective improvement of students’ course learning performance and recognition of the course was achieved.

With the rapid development of generative AI technology, it has been widely used in the field of higher education, especially in ideological and political education in universities (hereinafter referred to as IPE). From the perspective of responsible innovation, this study deeply discusses the influence of generative artificial intelligence (AI) on IPE in universities. This study empirically analyzes the practical application and effect of generative AI in university IPE through questionnaires, interviews and classroom observation. The results show that the introduction of generative AI improves students’ acceptance and classroom participation, which has a positive impact on teaching effect. At the same time, the research also reveals the social, environmental and ethical responsibilities that need to be paid attention to during the application of generative AI. By constructing structural equation model (SEM), this study further discusses the relationship between the use frequency of generative AI, students’ acceptance and the diversity of teachers’ teaching methods and the teaching effect, which provides useful reference and enlightenment for future educational reform and technology integration. In addition, this study also discusses the challenges and limitations of the application of generative AI in university IPE, and puts forward corresponding suggestions to promote its effective application and development in the field of education.

With the rapid development of artificial intelligence technology, ideological and political education in Chinese universities is undergoing profound change. This article is based on a three-layer concept of value resonance, situational perception, and adaptive presentation of educational resources. It explores how to use natural language processing, knowledge graphs, and reinforcement learning to construct an intelligent education framework to enhance the effectiveness of ideological and political education. This framework captures students' interest signals and emotional clues through multisource data, dynamically optimizes the order of resource presentation using a strategy network, and promotes value internalization and behavior transformation through a closed-loop feedback mechanism. The results show that the system demonstrated significant advantages at accelerating reinforcement learning convergence, accurately reaching emotional resonance, and enhancing classroom interaction. This study introduces new possibilities for the deployment of intelligent teaching systems.

迈入大数据时代，数据驱动的高校思政课精准教学无疑成为了这一时代教学范式的必然变革。它不仅能够满足学生多样化的学习需求，还能避免评价中的“功利化”和“形式化”倾向，推动教学范式向科学化方向发展。然而，由于大数据应用具有双刃剑的特性，高校思政课精准教学也面临着前所未有的挑战。具体表现为学生认知偏差与数据收集的困难、教师数据素养不足与教学实施的短板、教材内容与数据应用的脱节、数据隐私与安全问题的严峻性、技术平台与资源支持的不足，以及教育主体协同不足与动态反馈机制的缺失。为解决数据驱动高校思政课精准教学的困境，需要形成以学生为中心的数字育人共识，打造一支智能型思政课教师队伍，建立完善的数据管理制度，并推动优质数字育人资源的均衡配置，构建“数据–教学–评价”动态反馈闭环机制，从而促进高校思政课精准教学的优化与完善。Entering the era of big data, the data-driven precision teaching of ideological and political courses in colleges and universities has undoubtedly become an inevitable change in the teaching paradigm of this era. It can not only meet the diversified learning needs of students, but also avoid the tendency of “utilitarianism” and “formalization” in evaluation, and promote the development of teaching paradigm to the scientific direction. However, because the application of big data has the characteristics of double-edged sword, the precision teaching of ideological and political courses in colleges and universities is also facing unprecedented challenges. Specifically, it is manifested in the cognitive bias of students and the difficulty of data collection, the lack of data literacy of teachers and the shortcomings of teaching implementation, the disconnect between textbook content and data application, the severity of data privacy and security issues, the lack of technical platform and resource support, and the lack of cooperation and dynamic feedback mechanism of educational subjects. In order to solve the dilemma of data-driven accurate teaching of ideological and political courses in colleges and universities, it is necessary to form a student-centered digital education consensus, build an intelligent team of ideological and political teachers, establish a sound data management system, promote the balanced allocation of high-quality digital education resources, and build a dynamic feedback closed-loop mechanism of “data-teaching-evaluation”. So as to promote the optimization and perfection of precise teaching of ideological and political courses in colleges and universities.

Colleges and universities increasingly incorporate ideological and political (IP) concepts into their courses as a fundamental prerequisite and a rising IP education trend under changing conditions. Students have difficulty sifting through the ever-growing amount of online information to locate what they need in learning resources. Technology-enhanced learning encompasses any technology that helps students study more effectively. This paper suggests a personalized learning resource recommendation system (PLRRS) for IPC. Personal learning recommendation systems (PLRSs) that do their task well will help students cope with the existing information overload. They will make sure that they receive the correct information at the right time and in the right format for their particular needs. E-learning systems that intentionally personalize their courses to the preferences, objectives, skills, and interests of the students they serve are engaging in personalized learning. In the last several years, researchers have been looking at ways to assist instructors in enhancing e-learning. Personalized learning scenarios are created by picking the most relevant learning objects based on an individual’s profile. A test score greatly improved for students in IPC after using the model in this research, which suggests that this model has a strong promotion value.

The digitization of thought theory is not yet sufficient to meet the needs of the students. It is very necessary to strengthen the construction of ideological and political (IP) courses, strengthen the education of mainstream ideology, and occupy the initiative of discourse. There are effective ways and means to study the deep integration of information technologies into the new age of philosophy and philosophy education of students, which can greatly improve the quality of teaching and the effectiveness of humanities courses. The intuitive development of intellectual and political education through artificial intelligence is both a real prerequisite for modern development and technological innovation and for new ideas confronting the specific problems of thought-politics education, and it is a necessary prerequisite to ensure the quality and efficiency to improve the teaching of thought politics. AI in ways that embed technology provides a powerful impetus for contradictory movements in thought-politics discourse that lead the effective coordination of its internal elements to high-quality developments. In a practical way, we should take full advantage of the technical advantages of artificial intelligence, through intelligent mehrfachanalyse corresponding algorithms, artificial intelligenzbilder, and artificial roboterbilder photos and profiles that are used to write and accurately provide more precise, leading, stacking, and accurate estimates to maximize and to improve the accuracy of thought and policy education. Philosophy and political theory are the keys to guiding people to accomplish basic human tasks. With the passage of time and with continuous innovation, ideology and philosophical principles are both prerequisites for the self-development of the emotions of the age and necessary for improving the outcomes and effectiveness of scientific education. Advances in the field of artificial intelligence have fundamentally changed human life and have also impacted traditional school and university education systems. This brings new opportunities and challenges to students. This includes the use of smart technology to improve the learning process. Ideological teachers and policymakers must keep up with the all-round trend of the times, make full use of the benefits brought by intelligent new technologies and platforms, effectively improve the effect of ideological education, and further increase the attractiveness, attraction, persuasion, and contagion of ideological education.

Ideological and political education is an important part of higher education in China. Knowledge mapping is to connect all kinds of information to form a relationship network. This paper puts forward a teaching method of ideological and political courses in colleges and universities based on knowledge atlas. The knowledge atlas is used to relate the ideological and political education of the curriculum, build a multi-level knowledge system, define, organize, manage and transform the abstract knowledge, attributes, associations and other information into a real database, integrate the teaching content and relevant ideological and political content of the curriculum, give full play to the educational function of the professional courses of data base, and overcome the difficulties in the ideological and political education of technical courses, The teaching design and practice exploration of "ideological and political curriculum" were carried out.

Knowledge graphs and large language models (LLMs) have become important tools for educational innovation. This paper explores the application of these two technologies in the construction of ideological and political education in university courses. The paper begins by analyzing the importance of course-based ideological and political education and the challenges currently faced. It then introduces the role of knowledge graphs in integrating educational resources and constructing knowledge systems, as well as the potential and current status of LLMs in natural language processing and providing personalized educational content. This study presents a method that integrates the use of knowledge graphs and LLMs to construct resources and application systems for course-based ideological and political education. The results of practical case studies demonstrate that the proposed method improves the efficiency of constructing ideological and political education content, enhances the effectiveness of moral education within courses, and contributes to the innovative development of ideological and political education.

This study aims to construct a new model of ideological and political education in foreign language courses based on knowledge graphs and the ADDIE Framework. The "Guidelines for Ideological and Political Construction of Courses in Higher Education Institutions" emphasize the importance of ideological and political education across all disciplines and specialties in universities. However, current ideological and political education in foreign language courses faces several challenges, including the separation of course content and ideological elements, the absence of Chinese cultural elements in textbooks, and the singularity of teaching methods. By utilizing knowledge graphs, high-quality ideological and political elements are integrated into the foreign language curriculum, achieving an organic fusion of ideological education and course knowledge. This model is based on the ADDIE instructional design principle, which includes five stages: Analysis, Design, Development, Implementation, and Evaluation. It also incorporates constructivist theory, emphasizing active student participation and interaction.

教育数字化转型已成为不可逆转的趋势。知识图谱作为人工智能的基础性技术架构，能够有效连接，将资源数字化，可为课程思政的协同育人提供创新性解决方案。为提高数字时代生物医学基础课程思政教育效果，本文以知识图谱为基础构思生物医学课程思政的改革路径，分析课程思政的内涵，包括生物医学的思政教学特征属性、思政维度模型、思政价值塑造的内容及方式。以思政融入知识图谱为切入点，探讨基于知识图谱的生物医学课程思政改革思路，旨在通过本文为其他高校同类课程思政改革提供参考。The digital transformation of education has become an irreversible trend. As a fundamental AI-driven technological framework, knowledge graphs effectively interconnect and digitize educational resources, offering an innovative solution for collaborative ideological and political education (referred to as “curriculum ideology and politics”). To enhance the effectiveness of ideological education in foundational biomedical courses in the digital era, this paper proposes a reform approach based on knowledge graphs, analyzing the essence of curriculum ideology and politics, the distinctive pedagogical attributes of biomedical education, a multi-dimensional ideological education model, and the methods for shaping values. By integrating ideological elements into knowledge graphs, this study explores a reform strategy for ideological education in biomedical courses, aiming to provide a reference for similar curriculum reforms in higher education institutions.

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As an important position in education, distance education is a new subject faced by the education community under the new situation. In view of the problem of "islandization" in the process of ideological and political education in distance education based on big data technology, this paper proposes a personalized learning service framework for distance education students' ideological and political courses, including data management, data analysis layer and service provision. At first, a large amount of remote education course ideological and political data is collected through the network, database and various mobile platforms. After the data is preprocessed, knowledge graphs and data mining techniques are used to mine the ideological and political elements of the course and develop personalized ideological and political learning program. Distance education curriculum ideology and politics are supported to teach students according to their aptitudes, and focus on students' individualized ideology and politics learning, then improve the teaching effect of distance education ideology and politics lessons.

Under the background of widespread digital teaching, ideological and political education in higher education institutions faces challenges in delivering targeted resources and personalised recommendations. Traditional manual recommendation methods are no longer sufficient to meet demand, and knowledge graph-based recommendation offers a novel approach to addressing this challenge. However, existing research exhibits shortcomings in knowledge graph construction, recommendation algorithms, scenario adaptation, and experimental validation. This study constructs and validates a knowledge graph-based personalised recommendation framework for ideological and political education in higher education institutions. This framework integrates multi-dimensional entities encompassing resources, users, and scenarios in knowledge graph construction. The recommendation algorithm combines path features from the knowledge graph with user profiling. Scenario adaptation aligns with the ideological and political education objective of ‘value guidance’ to accommodate students' intellectual dynamics. Experimental validation is based on authentic data from university ideological and political courses alongside student behavioural data. The functional layer of this system encompasses the user interface, knowledge graph, data management, algorithms, database, and resource repository. Core modules include the ideological and political education knowledge graph, data management, personalised recommendation algorithms, and feedback optimisation. This system enables the systematic management and precise recommendation of ideological and political resources, providing technological support for tailored teaching approaches in higher education institutions. It holds significant practical value in advancing the digital transformation and enhancing the quality of ideological and political education within universities.

Abstract Based on the feasibility of a personalized teaching platform and user requirements, this paper puts forward the overall architecture design and database design scheme of a personalized teaching platform in ideological and political education, which mainly consists of three functional modules, namely, the knowledge mapping module, ideological and political education course recommendation module, and learning effect evaluation module. After crawling the initial data based on the LTP model, the ideological and political education course resources were extracted and integrated to complete the construction of the knowledge graph module. The ideological and political education course recommendation module is created using the KGCNN algorithm, and then the learning effect evaluation module is constructed by combining the online behavior of students. After testing the system’s performance, the application effect of the teaching platform is assessed. The results show that KGCNN aggregation layer space in the interval of 10~100 can embed data with power law distribution more effectively, and the KGCNN algorithm also has certain advantages in the field of modeling personalized teaching platforms for ideological and political education. The number of experimental classes and ordinary classes with final grades in the L1 band increased by 21.90% and 7.17%, respectively, compared to the midterm, indicating that the personalized teaching platform for ideological and political education courses can effectively promote the improvement of student’s academic performance, and the enhancement effect of students with high levels is more significant.

The growth of Artificial Intelligence technology has led to great changes in education. However, the large number of ideological and political education materials, along with complex learning resources, often cause poor learning efficiency and less student involvement. In this study, we introduced the knowledge graph technology to improve the organization and presentation of educational materials, solving the issues of repetition and lack of easy access to ideological and political knowledge. We combined data from university courses and online learning platforms to help knowledge extraction and graph fusion. The knowledge graph provides new insights and helpful ways for improving ideological and political education and supporting the growth of related fields in higher education in China.

This paper explores the integration of knowledge graph and personalized path recommendation to provide theoretical support and practical guidance for the innovation of ideological and political education in universities. Firstly, by constructing an ideological and political education knowledge graph, seamless integration of online and offline resources is achieved to provide a comprehensive ideological and political education experience. Simultaneously, an intelligent ideological resource repository is developed to utilize the knowledge graph for diversified resource retrieval and personalized recommendations. Secondly, based on the job-specific ideological literacy repository and student learning records, personalized learning paths are automatically generated using knowledge graph technology. This path includes resource recommendations, practical activity arrangements, and interactive communication, providing students with precise learning support and effectively enhancing ideological and political qualities. This study deeply integrates artificial intelligence technology with ideological and political education, offering new ideas for the modernization transformation and personalized teaching in university ideological and political education.

In the face of the transformation of the information receiving mode of Generation Z students and the demand for the precision of ideological and political education, this study constructs a two-wheel drive model of ``knowledge graph + topic evolution analysis" to explore the innovation path of artificial intelligence technology empowering ideological and political education. The topic evolution monitoring model is constructed based on dynamic Bayesian network (prediction accuracy is 87.3%). Empirical research shows that in the pilot application of three universities, the intelligent ideological and political system improves the average score of students 'theoretical knowledge test by 15.2 points (p<0.01), the score of values identity scale by 23.7%, and the efficiency of teachers 'lesson preparation by 40%. It is found that the knowledge graph can effectively solve the problem of knowledge fragmentation of ideological and political education, and the topic evolution analysis can accurately capture the life cycle law of hot topics such as ``20 Spirits" (timeliness is increased by 60%).The values identity scale adopted the validated 20-item questionnaire developed by Wang et al. (2021), consisting of four dimensions: socialist core values identification (5 items), political identity (6 items), cultural identity (4 items), and moral values (5 items). Scores were quantified on a 5-point Likert scale (1=strongly disagree, 5=strongly agree), with Cronbach's α=0.89 for internal consistency. Aiming at the problems of algorithm bias and data security in technical applications, the optimization strategies of constructing an ethical review committee, an interpretability model, and a blockchain certificate storage system were proposed. The research provided a theoretical framework and practical path for the digital transformation of ideological and political education, and promoted the transformation of educational decision-making from experience-driven to data-driven.

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Technologies such as machine learning and big data analysis of artificial intelligence have made it possible for ideological and political education to collect data accurately, monitor the process intelligently and deliver resources in a personalized way, forming a crucial path to respond to the education digitalization strategy and improve the effectiveness of ideological and political education. However, the current integration of the two is still plagued by problems such as insufficient depth, single application scenarios, weak technical adaptability and lack of ethical prevention and control. How to construct a scientific integration mechanism and innovative application paths has become an urgent subject for the high-quality development of ideological and political education.

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The currently used resource recommendation algorithm mainly recommends resources according to the user's preference for a tag class, ignoring the relationship between user preferences and needs and learning scenarios under mobile learning, resulting in poor efficiency and accuracy of recommended resources. In order to improve the shortcomings of the algorithm, this paper studies the personalized recommendation algorithm of Ideological and political teaching multimedia resources based on mobile learning. By constructing the map of Ideological and political teaching knowledge, this paper analyzes the correlation between resources. The diagnosis result of students' cognitive level is one of the characteristics of personalized recommendation. Mobile learning devices are used to collect data, calculate and perceive mobile learning scenarios. By improving the collaborative filtering technology, the teaching resources of Ideological and political courses can be personalized recommended. In the algorithm experiment, the average absolute error of the algorithm recommendation is relatively reduced by about 14.67%, the recommendation efficiency is higher, and the personalized recommendation effect is better.

In order to improve the personalized recommendation effect of ideological and political teaching resources in courses, a multi-layer microservice architecture recommendation system was constructed based on data mining technology. The system achieves multimodal data collection through distributed crawlers, extracts semantic features using BERT BiLSTM model, and combines dynamic construction of knowledge graph and user profile to achieve precise resource matching. Adopting a dual layer recommendation model, the improved collaborative filtering algorithm is integrated with the Wide&Deep model to enhance recommendation accuracy. The experimental results show that the system outperforms traditional methods in multiple performance indicators, especially in cold start scenarios where the effect is significant. Research shows that through multi-dimensional feature fusion and dynamic interest capture, the system can effectively improve user learning time and resource click through rate, providing an intelligent solution for recommending ideological and political teaching resources.

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Nowadays, the rapid growth of Information Technology (IT) advancements, various network sciences and personalized learning has become a new trend in modern education. However, a multi-dimensional evaluation feedback system is lacking on learning in school ideological and political education, and failing to provide sufficient carrier support to enhance educational effectiveness. Hence, this research provides a Knowledge-Based Filtering Recommendation System for Personalized Teaching Resources in Ideological and Political Theories teaching in all courses English Courses (KBF-PTRIP). This research has taken input data from Edx dataset and given to content extraction as user input. Here, the user inputs are analyzed and the similarity between learning resources and individual learners is calculated to provide the prediction score. Then, Term Frequency-Inverse Document Frequency (TF-IDF) method is exploited to select best features with the highest prediction score. Finally, the personalized teaching resources are recommended to the user with the help of Knowledge-Based Filtering (KBF) recommender. From the results, the proposed KBF- PTRIP model achieved best results in terms of accuracy 98.82%, recall 98.73% and F1 score 96.35% respectively by comparing with the existing Primary and Secondary Education Resource System by using Collaborative Filtering (PSERS-CF) model.

The personalized recommendation system influences the recommendation of ideological and political teaching resources in universities, resulting in a high MAE score. As a result, under the school-enterprise collaboration paradigm, this study proposes a customised recommendation approach for ideological and political teaching resources in colleges and universities. The ideological and political teaching resource bank is developed against the backdrop of the teaching paradigm that combines universities and businesses. Learners’ browsing data history is gathered to create a learning interest model for them. A hybrid collaborative filtering recommendation method was devised, and a recommendation engine was established by Taste component, taking into account individualised resource recommendation needs and information entropy weight distribution mode. When compared to previous techniques, the developed customised recommendation method considerably enhances the recommendation quality of instructional resources and reduces MAE by 29% and 34%, respectively.

The creation of a personalized ideological and political education resource suggestion system has significant practical implications for boosting the efficiency of ideological and political education. Many challenges are preventing ideological and political education from progressing smoothly in the online environment, and the mainstream values of education are being affected as an effect. The educational model is reversed, there is a single way to communicate, and the educational content is solidified. It constructs a personalized recommendation user model for educational resources, and then builds a recommendation collaborative filtering algorithm using Data Mining (DM), that produces personalized suggestion of ideological and political education resources, improving the Collaborative Filtering Algorithm (CFA) allows for personalized recommendations of teaching materials for ideological and political education. As a result, DM-CFA has become a tool for education professionals can gain a better understanding of the learning and life characteristics of current college students through the collaborative operate of ideological and political education entities in the network environment, that likewise offers a diverse range of educational materials and varieties. As a result of the curriculum’s ideological function in mounding students’ brains, the ruling class’s history, gender, religion and nationality develop in society. Developing various learning channels for pupils can increase their interest for ideological and political research and help students grow holistically.

The rapid development and wide application of artificial intelligence gives ideological and political course a window to reform. The integration of artificial intelligence and ideological and political education conforms the trend of social advancement, meets the demands of personalized learning, strengthens the efficiency and practicality of ideological and political course, and facilitates the sharing of educational resources. But it also faces some challenges, hence this paper analyzes the challenges encountered in the integration process of artificial intelligence with ideological and political education from three perspectives: the teaching subject (teachers), the learning subject (students), and artificial intelligence. Then it provides recommendations to address these challenges.

This paper aims to design and implement the online political and ideological teaching system based on personalized recommendation in order to more accurately recommend teaching resources appropriate for students' learning, thus improving the learning efficiency and teaching quality of the online political and ideological teaching system. First, the design of the online political and ideological education system is detailed, along with its basic framework, functional modules, hierarchical structure, and database. A personalized recommendation approach based on knowledge map is proposed. The algorithm is applied to the online political and ideological teaching system to understand the differences of students' interests in different teaching resources, establish a student interest transfer model, and effectively improve the transfer of students' interests. On the basis of knowledge map, the matrix decomposition method is introduced, matched with the knowledge map to obtain the recommendation prediction score, and the feedback model is established and extended. Measure the dynamic transformation of the recommended ideological and political teaching content, and comprehensively consider the long-term and short-term preferences of students, so as to realize the personalized recommendation of ideological and political teaching resources. Experiments show that the personalized recommendation online political and ideological teaching system designed in this paper has good overall performance, the accuracy of the proposed recommendation approach is high, and the recommendation time is fast, so as to improve the teaching quality of the teaching system.

The ideological and political course should not only keep the academic rationality and political nature of the course itself, but also take into account the characteristics of colleges and universities and students’ growth and development needs. At present, there are some problems in the curriculum of ideology, such as mechanical rigidity, weak pertinence, lack of synergy, and inability to form a personalized collaborative and precise education mechanism. Aiming at related problems, this article constructs an accurate teaching model of ideological and political course based on collaborative filtering algorithm. First, the public test set of recommended fields is used to test and verify the effectiveness and practicability of the algorithm. For the data sparseness and cold start of collaborative filtering algorithm, the course feature attributes and attribute value preference matrix are used to solve the problem, and the similarity is calculated offline, so as to realize the real-time recommendation and accurate teaching of the course. In order to verify the effectiveness of this method for precise teaching, we conducted a test. The test results show that the precise teaching model has a positive effect on the improvement in students’ academic performance. The method proposed in this article realizes the identity transformation of students from passive acceptance to active construction, and the teaching effectiveness of ideological and political course is effectively improved.

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Nowadays, the evaluation of education quality has become a pressing concern globally, driven by the need to ensure that educational institutions provide students with knowledge. Traditional approaches for recommendation of teaching resources for ideological and political courses had faced several challenges which include lack of personalization and limited contextual understanding. Therefore, this research proposes Normalized Discounted Cumulative Gain (NDCG) for recommendation of teaching resources for ideological and political courses which is employed on Open Educational Resources (OER) dataset. Then, content is extracted by using Natural Language Tool Kit (NLTK) for annotated documents which helped in identifying key components. After that, features are selected by using Proximal Policy Optimization (PPO) which prevent overfitting. Next, the realization of Deep Reinforcement Learning (DRL) evaluated fine-tuning and enhanced student performance and instructor satisfaction with expected reward through Policy Gradient Method (PGM). Finally, NDCG evaluates both relevance and position of recommended data with updated gradient using distribution over the action space of literature courses. The proposed NDCG achieved better results in accuracy (98.22%) along with a recall (89.90%) and an Fl-score (95.45%) when compared with existing Bayesian Personalized Ranking Matrix Factorization (BPRMF).

Abstract The advancement of the high-quality development of the blended teaching of ideological and political courses in colleges and universities, as well as the thorough integration of artificial intelligence with this teaching approach, are imperative demands of the modern era. In order to analyze the specific applications of artificial intelligence technology in the teaching of ideological and political courses in colleges and universities, this paper first establishes the general framework for their application. It then proceeds, module by module, from the individual courses and examines how they are taught. Data mining techniques are used to extract the characteristics of the ideological and political course teaching resources, determine the similarity between learners, and complete the personalized intelligent recommendation of ideological and political course teaching resources in the personalized ideological and political course learning module. The ideological and political course teaching resources recommendation model is constructed through artificial intelligence technology. The text responses from the students are processed using natural language technology, and the learning effect is predicted using a logistic regression model. To build the BF-BKT knowledge tracking model, which tracks student feedback during the learning process of ideological and political courses, incorporating behavioral and forgetting elements. The purpose of the teaching framework is to examine the results of combining political and ideological courses with AI. The results indicate that the student’s critical thinking score is 46.6245 after incorporating artificial intelligence, which is roughly 6 points higher than the traditional technique. Following the use of AI fusion, the students’ three viewpoints and their cognition of family and national sentiment improved, with increases of 4.9 and 4.7 points, respectively, in the pre-and post-tests. It is evident that the integration of political and ideological courses at the university with artificial intelligence aids in students’ formation of their three points of view and emotional development, ultimately leading to cognitive characterization.

To accurately grasp the characteristics and needs of the object, and enhance the pertinence and effectiveness of evening teaching of Ideological and political theory course, this paper designs an online teaching platform assisted by big data technology. According to the actual needs of the network teaching model, we make the overall planning and design of the functional modules of the platform. Then, in the key module of user classification and location, feature sets related to learning behavior are extracted from learners' behavior data, and a personalized model of user portrait based on improved K-means algorithm is constructed. Through the big data platform based on Hadoop distributed cluster, combined with data analysis algorithm, the data set is cleaned, standardized, analyzed and processed, and the user interests and preferences are analyzed. Finally, the function of the system is tested through web development, and the results show that our scheme can depict the different characteristics of learners, which provides a scientific basis for personalized recommendation and auxiliary learning of Ideological and political education.

In response to the problems of "efficiency bottleneck" and insufficient "personalization" in ideological and political education in universities under the background of the information age, this project innovatively integrates data mining and machine learning methods to establish a deep level user behavior analysis and recommendation method system based on big data. It fully collects students’ online learning trajectories and social media data, and accurately captures individual interests and needs through refined portrait construction and cluster analysis. On this basis, the optimal collaborative screening technique is adopted to personalize the matching and recommendation of different types of educational resources, and provide students with personalized learning paths. The experimental results show that this teaching mode can fully mobilize students’ learning enthusiasm, improve the pertinence and effectiveness of teaching, and the average academic performance of students is between 3-7 points, which tests its overall effect. The research results of this project will further expand the application scope of educational recommendation algorithms, provide new ideas for optimizing resource allocation and improving the quality of personalized education, and provide important technical and theoretical support for promoting the reform and development of ideological and political education in Chinese universities.

Political and Ideological teaching is a social practice that uses political views, moral norms, and ideas to improve students' ideology and conform to social norms for Ideological and political curriculum learning resources. Traditional approaches to ideological and political education are ineffective because no proper personalized recommendations exist. Therefore, adapting to individual learners' needs, interests, and learning styles is difficult, and cold start challenges arise once a novel user joins a recommendation system without any historical data. In this paper, Deep Reinforcement Learning (DRL) is proposed to recommend mobile political and ideological education. Initially, the data is taken from the edX dataset followed by content extraction including recommendations based on historical records. Then, the feature selection with Recursive Feature Elimination (RFE) is used to select optimal recommendations from content. After that, the DRL is introduced to determine the selected features based on the chi-square value with the help of the chi-square test. Finally, the recommendation algorithm evaluates the value of the chi-square with corresponding recommendations generated from content extraction and recommends better mobile political, and ideological education. From the results, the DRL gives more accurate results in contrast with the existing Analytic Hierarchy Process (AHP) model in terms of accuracy (98.22%), recall (89.90%), and f1-score (95.45%) respectively.

Abstract The application of big data technology in the teaching of ideology and politics courses in colleges and universities can provide strong support for the precise reform and innovation of ideology and politics work. In this paper, we first sort out the status quo and dilemma of the teaching of ideology and politics courses in colleges and universities and analyze the advantages and feasibility of big data technology in the learning of ideology and politics. Then, based on the big data streaming computing Spark framework, a personalized learning service platform for teaching Civics and Politics courses in colleges and universities was established. Aiming at the online learning behavior of students on the platform, the K-mean algorithm is used to select their behavioral characteristics. The personalized recommendation of learning resources for Civics and Political Science courses is implemented based on the learning behavior. Data was utilized to analyze the effectiveness of the personalized learning platform in transforming the teaching of Civics and Political Science courses in colleges and universities using S University as the research object. The study found that the number of focused learners in category 1 of the online learning behavior reached 303, and the teaching of the Civic and Political Science course based on the personalized learning platform helped the students’ will cultivation score increase by 2.49 points and more than 95% of the students recognized the platform. The customized service learning platform for teaching Civics courses established based on big data can realize the innovative transformation of Civics courses in colleges and universities and improve the teaching quality and level of Civics courses in colleges and universities.

With the rapid development of information technology, digital transformation in education has become an important way to improve the quality and effect of education. Especially in the ideological and political course teaching, how to use intelligent technology to improve the personalized recommendation effect of teaching resources has become a hot research topic. In this paper, we first describe the concepts related to deep reinforcement learning. Secondly, an improved algorithm of ideological and political course resource recommendation based on deep reinforcement learning is extracted. Finally, the effectiveness of the proposed system in improving recommendation accuracy and personalization is verified through experiments. This paper not only provides theoretical basis for intelligent recommendation of ideological and political education resources, but also provides practical reference for personalized learning in education field.

Abstract This paper designs a teaching mode for online ideological and political education under deep learning, designing teaching content in a structured, contextualized and activity-based way to enhance teaching effectiveness and learning experience. By mining the learning needs embedded in users’ learning behaviors, customized learning resources are provided for each student to meet the personalized learning needs of different students. It also uses knowledge-forgetting matrix decomposition technology to identify and recommend key knowledge points in teaching content, helping students master important knowledge more effectively. The teaching mode proposed in this paper performs well in resource recommendation, with an average server response time of 15.147ms, while the students’ preference time is above 0.940s, which effectively improves the educational and teaching effect of the theory and method of online ideological and political education for college students.

Abstract The innovative development of the Civics and Political Science class against the backdrop of artificial intelligence represents a relatively new research direction within the academic world. This paper designs a teaching aid system for civics and political science classes based on artificial intelligence technology, with the main functions of accurate teaching intervention and personalized course resource recommendations. We incorporate the reinforcement learning algorithm into the decision-making process for accurate teaching interventions, build a decision-making model for these interventions based on reinforcement learning, and utilize the Q-learning algorithm to model the decision-making model’s data. Construct the Civics and Politics course resource network, update the keyword weights of the learning interest nodes, mine the interest features, and complete the personalized recommendation of the Civics and Politics course resources in colleges and universities based on collaborative filtering. We apply the system of this paper to the practice of teaching civics and politics to 50 first-year students majoring in ideology and politics at University D. After the practice, the students achieved an overall assessment score of 88.76, a 6.25 improvement over their pre-practice score, and demonstrated improved behavior and civics learning outcomes in the “excellent learners” and “potential learners” categories. The average satisfaction levels in the four aspects of system design, application effect, function evaluation, and function implementation effect are 4.625, 4.635, 4.627, and 4.647, respectively, with a high level of satisfaction.

To address the existing challenges in the teaching ideological and political assessment system within Chinese universities, particularly in the context of New Engineering disciplines, this study proposes a new six-dimensional teaching ideological and political evaluation framework. This framework is designed to provide a comprehensive, systematic, and scientifically grounded approach to evaluating the integration of ideological and political education into engineering curricula. By focusing on six key dimensions—socialist core values, Chinese excellent traditional culture, professional ethics and engineering ethics, craftsmanship and creative awareness, teamwork and communication skills, and social responsibility and patriotism—the framework ensures that ideological and political education is not only embedded in the curriculum but also effectively assessed and continuously improved. To operationalize this framework, a teaching ideological and political evaluation system was designed and implemented, leveraging multimodal data-driven algorithms. This system integrates diverse data types, such as text, images, audio, video, and behavioral data, to provide a holistic and objective assessment of teaching effectiveness. The use of advanced data mining and machine learning techniques enables the system to analyze complex datasets, extract meaningful insights, and generate actionable feedback for educators and administrators. The introduction of this pedagogical and political evaluation system provides a scientific and efficient evaluation system for educational administrators to assess potentially valuable ideological and political education in the teaching and learning process and apply it to the management of ideological and political work.

Abstract Based on a multimodal topic network, this paper constructs a knowledge combination model for the cross-discipline of public sports and civic politics. Through multimodal data fusion, the knowledge combination between the two kinds of public sports courses and Civic and political education in colleges and universities is obtained. Finally, using the China Knowledge Network database as the source of data retrieval, the hot spots and their evolution paths of Civic Politics in public sports courses were explored so as to excavate the elements of Civic Politics in Civic Politics of Public Sports Courses. The results show that among the elements of ideology and politics in the curriculum, “patriotism education” has the greatest emergence intensity, with a value of 49.382, followed by “establishing moral character” with an emergence intensity of 16.338, which indicates that the main purpose of the construction of the elements of ideology and politics in the physical education curriculum is to cooperate with the orientation and requirements of ideological and political education, which is the most important part of the public sports curriculum. It shows that the main purpose of the construction of the ideological and political elements of the physical education curriculum is to match the orientation and requirements of ideological and political education and to clearly link the goal of one’s struggle with the destiny of the state and nation.

In response to the core issues of strong subjectivity and single dimensionality in traditional evaluation methods for ideological and political education, this paper proposes an evaluation model based on multimodal data fusion and an attention mechanism, named the Multimodal Attention-based Ideological and Political Education Evaluation (MA-IEE). This model aims to achieve an objective and quantitative assessment of educational effectiveness by intelligently analyzing students' multimodal behavioral data (text, speech, vision) within an educational context, integrating deep learning with a cross-modal attention mechanism. In practical teaching applications, this model can serve as a formative assessment tool to provide teachers with immediate, fine-grained feedback on students' classroom reactions (such as emotional states) and learning engagement. This helps educators dynamically optimize teaching strategies, thereby connecting abstract educational goals with observable and analyzable student behavioral data, rather than being used merely for summative judgments. To verify the technical feasibility of the model, extensive comparative experiments and ablation studies were conducted on the publicly available multimodal emotion analysis datasets, CMU-MOSEI and IEMOCAP. The results indicate that the proposed MA-IEE model outperforms models based on single modalities and traditional fusion methods across key metrics such as accuracy, precision, recall, and F1-score, thus validating its effectiveness and superiority in fusing multimodal behavioral data for quantitative evaluation.

With the continuous change of the national security situation, the importance of national security education in higher education is becoming more and more prominent. The purpose of this paper is to explore the evaluation system of the quality of civic teaching and its improvement path of national security education courses for college students based on multimodal sentiment analysis. The study first collects students’ affective feedback in the national security education course through questionnaires and classroom observation, and analyses the data using affective analysis techniques to reveal the relationship between students’ affective states and learning effects. The results showed that most students had a positive attitude towards the course content, but rated the sense of engagement and interactivity of the teaching methods as relatively low. In addition, there was a significant correlation between affective fluctuations and classroom engagement, indicating the importance of affective factors in the teaching and learning process. Based on these findings, this paper proposes the construction of a teaching quality evaluation system and corresponding suggestions for improvement, including the enhancement of classroom interaction, the use of modern technological means to promote emotional communication, and the establishment of a dynamic feedback mechanism. Through these measures, it aims to improve the teaching quality of national security education and enhance students' national security awareness and ideological and political literacy. Finally, this paper hopes to provide a reference for future related research and a practical guide for national security education curriculum reform in higher education.

Abstract Mental health education for college students is an important part of ideological and political work in colleges and universities, which is related to the physical and mental health and long‐term development of college students. In particular, the nature of the work of counselors endows them with unique advantages in psychological education. Based on the insufficiency of unimodal data features, we propose a method for analyzing the mental health of college students based on multimodal social‐affective classification. At the same time, we design a multimodal data fusion model, which takes text data as the main body and uses text and images to jointly classify the main body's emotion. First, we use the Bidirectional Encoder Representations from Transformers (BERT) pre‐training model to extract text features and obtain corresponding text vectors. Second, we utilize the Visual Geometry Group (VGG16) model trained on the ImageNet dataset as a pre‐training model to obtain image features. Third, we combine the modality features extracted by the two models to complete the final mental health classification task. Experimental results show that our proposed multimodal feature fusion model exhibits good performance on both constructed and public datasets.

With the framework of digital education, conventional approaches for civic and political education must be creative to satisfy evolving learning requirements. This work presents a new diagnostic model of students' performance in civic and political education (MCRD-IEEM), comprising four main layers and generates a thorough intelligent assessment system by means of multimodal features including behavioural data, knowledge mastery degree and emotional feedback. This work planned and carried out two tests to confirm the efficacy of the model: a multi-feature fusion effect analysis experiment and a model effect validation experiment both of which revealed that the MCRD-IEEM model was better than the comparison model in many facets. This paper offers theoretical support and practical advice for the creative application of educational technology and the digital transformation of the educational assessment system as well as a fresh perspective for the quality evaluation of ideological education.

In the context of the COVID-19 pandemic, online public opinion has had a profound impact on the ideological and political education management systems for university students. However, existing management systems often struggle to adapt to the rapid shifts in public sentiment and efficiently process the diverse forms of data generated through social media platforms. To address these challenges, we propose a model that integrates computational intelligence with multimodal data processing techniques, including multimodal fusion, meta-learning, attention mechanisms and reinforcement learning (RL). This model is designed to enhance the adaptability, decision stability and robustness of educational management systems by effectively analyzing and responding to complex and evolving online public opinion. The multimodal fusion enables the system to process different types of data, meta-learning improves the model’s adaptability to new information, attention mechanisms allow the system to focus on key features, and RL ensures that optimal decisions are made in real-time. Experimental results show that our model outperforms existing approaches in key performance metrics, including accuracy, robustness to noise and decision-making efficiency, particularly in the dynamic environment of online public discourse. This research contributes to the optimization of ideological and political education management systems by improving their responsiveness and effectiveness in the face of rapidly changing online public sentiment.

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

Abstract In this paper, after the extraction of teaching resource features by mutual information method, the classification of Civics teaching resources is realized by constructing a decision tree classifier. According to the current situation of Civics education in university sports courses, the set segmentation correlation function is determined, the contribution of each factor is combined using the correlation matrix, the correlation coefficient weights are calculated, and the design of the Civics multimodal corpus is accomplished based on big data technology. Nine universities in Guangzhou City were selected as the objects of this study, and the research data were obtained through questionnaire surveys of physical education teachers in these nine schools. SPSS26.0 and Origin 2019 software were used to study the Civics and Politics of Physical Education Courses in Colleges and Universities. The results show that the convergence speed of the objective function of the algorithm in this paper and the classification accuracy has been improved substantially, compared with other algorithms, by about 0.3 to 0.7, i.e., it shows that the algorithm in this paper has a good performance of classification of the course Sijian. The scores of national sentiment and sports core literacy are 0.76 and 0.76, respectively, with a rank of medium (less than 0.79), and the score of physical and mental health is 0.82, with a rank of high (greater than or equal to 0.8). That is, the public physical education (volleyball) class has a relatively satisfactory effect on the cultivation of students’ physical and mental health, while there is still room for progress in the cultivation effect of family and national sentiment and sports core literacy. This study supplements, to a certain extent, the lack of an evaluation index system for the effect of sports courses’ ideology and nurturing and promotes the better development of public sports courses’ ideology.