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The structure of energy consumption and reducing the carbon footprint has become an important issue in the field of carbon and energy conservation. This paper adopts spatial econometric Analysis to construct a framework for analyzing the influencing factors of carbon emissions based on the STIRPAT model. It applies the K-means algorithm to effectively cluster and classify the energy consumption of different regions. Then, the article analyzed these clustering results in depth using the Kaya constant equation to calculate the carbon emissions of each area. The results of the carbon footprint analysis reveal that the Gini coefficient of carbon emissions in the eastern region peaked at 0.352 in 2014, while decreasing to a low of 0.284 in 2019. the western and central areas have the highest Gini coefficients of carbon emissions at 0.271 and 0.248, respectively. furthermore, from 2015 to 2022, the ecological pressure on the carbon footprint of the whole industry has always remained at 3.033 above, reaching a historical high of 3.433 in 2022.The application of this paper not only helps to solve the problems in the existing carbon footprint evaluation methods, but also provides a scientific basis for more effective management and reduction of carbon emission.

With the development of the times, the cultivation of students’ comprehensive ability has been widely emphasized, among which the cultivation of music appreciation ability is more and more important. The purpose of this paper is to develop a framework for a multi-intelligences music appreciation ‘immersion’ teaching mode, which is based on the teaching reform of the music appreciation course. Our process involved collecting and pre-processing data related to music emotion, extracting music emotion features, and building a model for music emotion appreciation using long and short-term memory networks. The construction of a multi-featured memory network knowledge tracking model involved cross-fertilizing students’ responses to music appreciation difficulty and ability. Through the effect test of the memory network knowledge tracking model and the practical analysis of music appreciation curriculum reform, it can be seen that the average effect of the knowledge tracking model proposed in this paper is higher than that of the traditional knowledge tracking model by 0.15~0.21. The total score of the experimental class’s music appreciation ability is 10.21, while the control class’s score is 7.78, and the experimental class based on the teaching music appreciation curriculum of multiple intelligences is higher than that of the control class by 2.43%. This paper provides a reform direction for the teaching reform of music appreciation courses based on the multiple intelligences music appreciation teaching program, which is of practical significance for the development of music appreciation teaching.

This paper combines the knowledge related to the Knowledge Graph to store and manage the ideological knowledge in the schema layer in the form of an ontology repository. The Bootstrapping-based extraction method has been improved for entity relationship extraction at the data layer, and semantically constrained relationships have been added to reduce the problem of extraction bias. Civics education’s characteristic attributes are identified through attribute extraction using crowdsourcing. For the entity alignment problem in entity extraction, the relationship triad and attribute triad are jointly used for entity vector characterization, and the entity alignment model based on BERT and TransE is proposed. Finally, in the process of testing knowledge mapping, more than 70% of college students were satisfied, and the percentage of college students agreeing and strongly agreeing in the dimensions of knowledge construction were 68.81%, 77.92%, and 75.63%, respectively. In the six dimensions of positive psychological quality of college students who have used knowledge mapping, there is a significant increase in the data of the courage dimension from 3.4 to 4.14, and the other dimensions have also increased. The performance of the knowledge graph constructed in this paper for civic information education is outstanding.

To do a good job of school ideological and political work and ideology work in the Internet era, it is especially important to integrate the organic integration of Civic and Political teaching and student management. This paper takes H city as an example and establishes an evaluation index system for student management and informationalization of ideological and political teaching. After normalizing and quantifying the indicators, the entropy weight method is used to solve the weights of the indicators, and the coupling coordination degree model is established to judge the integration level of the two informatizations. To investigate the integration factors of the two, the gray correlation coefficient and the degree of correlation between the indicators are derived using the gray correlation method. The results show that the average annual growth rate of the integrated development level between the two is 4.42% and 8.15% during 2011-2020, and the coupling degree is higher on the whole, with an average of 0.8994, and the coupling degree of coordination is also on the trend of increasing year by year. The gray correlation between the two is high, in which the correlation between the working mechanism of cooperative education in the informatization of the work of Civic Teaching and the quality of practice indicators and student management reaches 0.7806 and 0.7876. To diversify, it is necessary to develop the integration of student management and Civic Teaching based on the research in this paper.

This paper proposes a logistic regression model with structural sparsity to study the characteristics of innovation and entrepreneurship among college students. The article first analyzes the basic form of the logistic regression model, including the objective function and the selection method for the penalty function. Then, because the ADMM algorithm combines the advantages of augmented Lagrangian and pairwise decomposition, which can reduce the computational difficulty and complexity, based on this advantage, this paper designs the ADMM algorithm solution framework that is favorable for distributed computing. Finally, this paper analyzes the relationship between the development of innovation and entrepreneurship ability of students in R colleges and universities and their gender, grade, academic foundation, experience in clubs and discipline type. The results yielded that college students’ mean value of innovation and entrepreneurship competence in HEI R was 3.734. The mean value of the scores of each sub-competence ranged from 3.531 to 3.918, which puts the overall innovation and entrepreneurship competence of students in this university at an intermediate level. Therefore, this study plays an important role in understanding the innovation and entrepreneurial characteristics of students in higher education.

The purpose of exploring the development mode of online teaching resources in colleges and universities is to enhance the utilization rate of online teaching resources to promote the diversification of teaching methods. In this paper, based on big data technology, a cloud education platform integrating online teaching resources in colleges and universities is constructed under the perspective of integrating industry and education, and the architecture and functions of the cloud education platform are explained. Then the collaborative filtering algorithm is used to filter and collect the information on online teaching resources, and the Pearson correlation formula is used to pre-process the data and help users to recommend teaching resources. Finally, the pre-processed data are filtered and analyzed using the multilayer perceptron technology under the deep learning model. To verify the practicality of the cloud education platform proposed in this paper, the platform’s performance is evaluated by combining MAE metrics with RMSE metrics. In terms of load capacity, the platform has the best load capacity when the number of users is 200. From the performance test, the average transmission rate of the cloud education platform is 8.1% and 0.93% higher than that of NetEase Cloud Classroom and Tencent Classroom, and the average transmission time is 7.09 s. From the practicality analysis, the average value of the MAE index is 0.317%, and the average value of the RMSE index is 0.232%. This shows that the cloud education platform can effectively integrate university online teaching resources and help universities achieve diversified development of teaching methods.

To study the direction of preschool students’ interest development, this paper proposes to mine and analyze preschool students’ interest development data using a deep learning model. This paper first introduces the basic algorithmic process of deep learning BP neural network model, then uses a genetic algorithm to optimize the traditional BP neural network to get the best performance. The deep learning model is then used to analyze the preschool students’ family income, family structure, interest cultivation direction, gender and age, and interest cultivation orientation and diversion. The lower the family income, the higher the percentage of children choosing interest classes, mostly concentrated in families with income between 2000-8000. In terms of gender, there are also differences in interest cultivation analysis, with boys favoring the cultivation of science and sports abilities such as logical thinking, technology, calculation, and sports, accounting for about 20% more than girls in general, while girls favoring the cultivation of art abilities such as dance, English, reading, and vocal music, with 15-20% more than boys. Deep learning model-based interest development for preschool students can follow the natural choices of young children and provide scientific guidance for interest triage of preschool children.

In this paper, a multi-branch deep network architecture (JESF-MBF) based on joint embedded semantic features is proposed. In order to construct the structural consistency between visual image space and label semantic space, as well as to ensure the correspondence between sketch or image data and labels, the JESF-MBF model uses cross-entropy-based multivariate classification loss and cosine distance-based semantic similarity loss for model optimization. The optimized model improves the ability of label semantic information representation and image visual information representation, alleviates the discrepancy of data domain distribution between sketch and image, and constructs the mapping relationship between visual embedding space and semantic embedding space, so the sketch-based zero-sample image retrieval task is realized based on the intermediate medium. In the comparative model analysis, the average evaluation index of JESF-MBF is 0.412, better than that of Doodle2search at 0.344. In terms of the teaching effectiveness of the teaching platform constructed in this paper, the average time spent for the experimental class to complete each subject work is 9.4 class periods, while the average time required for the experimental class is 19.7 class periods. The results show that the art teaching platform based on the JESF-MBF model is very effective in terms of performance as well as improving the art level.

This paper suggests a J2EE framework-based online intelligent diagnostic assessment scheme. The system has the capabilities of teaching, diagnosis, testing, and feedback as auxiliary teaching software through the automated alteration of subjective and objective questions and the individualized design of diagnostic results. Combined with the characteristics of English composition, the research focuses on the use of the shortest editing distance, cosine vector space, regular matching and other techniques to automate the composition correction and diagnosis, which not only realizes the final scoring of subjective questions but also the diagnostic report can provide students with personalized comments, precisely give the strengths and weaknesses in terms of knowledge points, as well as the analysis chart of knowledge points, scores and ability values of previous exams. In this paper, when evaluating the system with teacher ratings, the overall mistake rate for class essays was 9.0% when the similarity criterion S=0.2 was employed. For individuals and classes passing the test, the error was within 10%, and the score difference was within 2 points. Finally, the common errors of students in English writing were detected, with grammar errors accounting for 58.2%, vocabulary errors for 26.7%, and essay errors for 15.1%. Therefore, this system can make a good contribution to the teaching reform of English and achieve a real reduction of the burden for teachers and students.

As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no longer meet the current needs. This paper first divides assessment techniques into two main categories from an application perspective: assessment of student learning performance and in-depth cognitive diagnosis. Students are automatically provided with appropriate learning content based on their ability level and learning style, providing them with accurate and timely feedback. Secondly, a new fuzzy inference model is proposed to determine students’ student outcomes by addressing the obvious shortcomings of the fuzzy sets usually used for student outcome assessment. Finally, the validity and usefulness of its assessment model are verified by the student learning performance on a real data set. The results show that the fuzzy inference assessment model designed in this paper can obtain an assessment accuracy of 85.8% for the learner’s learning outcomes, which has a good assessment effect. And the fuzzy inference assessment model also retains the greatest advantage of linear fitting regression, which reflects the correlation between the parameters of students’ learning behaviors and the final learning outcomes. The assessment method based on the fuzzy inference model predicts learners’ learning risks and provides learning interventions in advance for smart learning, and also provides new ideas for deepening education reform.