This paper designs a multimodal data mining and learning behavior analysis model for civic education, uses improved clustering and association rule algorithms to analyze the multimodal data obtained from students, mines the basic consumption, learning and life behavior characteristics, and carries out analysis of the students’ civic situation in order to take targeted civic education measures. Aiming at the problem that traditional clustering results are greatly affected by the selection of initial clustering centers, Gaussian density function is used to determine the initial clustering centers, and Euclidean distance is replaced by density-sensitive distance to avoid sensitivity to noise and anomalies, which improves the accuracy of the clustering results of students’ behaviors. Then we use the FP-Growth association rule algorithm to improve the Apriori construction, recursively and iteratively construct the frequent pattern tree and get the final frequent item set, which improves the efficiency of student behavior data mining. After analyzing the processed student data of a university, it is found that most of the students have low interest in borrowing books, 38.22% of the students borrowed only 2.19 books on average, and the total number of times of book borrowing is only 5.4 times, and the average number of days of single borrowing is 62.3 days, and the school library needs to increase the promotion of students’ reading, which can be done through the way of offline book fairs and e-recommendations to improve students’ interest in reading books. Reading interest. The study makes a useful exploration for the informatization and intelligentization of ideological education in colleges and universities.

This paper puts forward countermeasures to maximize the ecological benefits of agroforestry resources from the perspective of sustainable development of urban agroforestry resources. Taking the maximization of ecological benefits as the goal, the optimal allocation of agricultural and forestry resources is carried out. Based on the results of the optimal allocation of water resources, the planting structure of crops in the irrigation area is adjusted with the water allocation of irrigated crops as the constraint. The optimization model under the constraint of eco-efficiency objective was constructed based on the variational method and optimal control model, and the model was solved by the method of Pontryagin’s great value. After the model adjustment in this paper, the planting structure of crops in the irrigation area of city A was obviously optimized, and the planting area of potatoes accounted for the largest share of the planting area of all the crops in the irrigation area, which was about 40.61%, and the ecological benefits of potato crops were higher, which got the priority of the model, and at the same time, the model also reduced the planting area of the crops with low ecological benefits, and this reasonable allocation adjustment method satisfied the goal of maximizing ecological benefits.

This paper combines the project response theory to dynamically adjust and update the resources according to the learning effect and learning feedback in the process of Civic Education, so as to achieve the goal of matching the learners with the learning resources and realize efficient learning. The differential artificial raindrop algorithm based on perturbation mechanism is designed to realize the solution of multi-objective combinatorial optimization of learning resource allocation. Performance experiments show that the convergence curve of the resource allocation algorithm in this paper is gradually flattened, and the algorithm still has the evolutionary ability, the convergence curve is still decreasing, and the final characteristic difference value is also better than other BPSOR and GAR algorithms. In the case of the number of learning resources of 10, 20, 30, 50, 100, the time consumed is 207ms, 1602ms, 20506ms, 68430ms, 354687, all of which are the lowest, and the success rate is also the highest in the model. The optimal learning path is applied to an experimental class in a university for a 6-week teaching experiment, and the experimental class scores 87.2 points in the Civics test, which is much higher than the control class. This paper realizes the accurate capture of students’ Civics learning problems and the recommendation of targeted teaching resources, which can improve the quality and effect of Civics teaching.

Due to differences in lifestyle, cultural capital and social support, foreign immigrants often have difficulty integrating into the ecology of their native communities and are limited in their space for development. To solve this difficulty, this paper applies the principle of regularization to obtain a logistic regression model by categorizing the factors affecting the social integration of foreign immigrants. The algorithms of log-likelihood function and negative Hessian matrix are used to optimize the parameters of the model, construct the multivariate logistic regression model based on the social integration of foreign immigrants, and analyze the regression results among various factors. The success rate of foreign immigrants’ local integration is higher when the immigration-related system is more perfect, the foreign immigrants’ cultural identification with the local area is higher, the cognitive deviation between foreign immigrants and locals is smaller, and the community integration structure is more appropriate. The highest correlation between the factors affecting the social integration of foreign immigrants is the formation of ethnic networks that are not embedded in the community by foreign immigrants who “embrace the group”, and the cognitive bias of local residents towards foreign immigrants, with a correlation coefficient of 0.9214, and the correlation coefficients of the rest of the indicators are less than 0.9. This paper classifies the migrants into “migrants of work nature” and “migrants of employment nature” in accordance with the purpose of their migratory activities. In this paper, according to the purpose of migration, migrants are classified into four categories: “work migration, study migration, investment migration and shelter migration”, and the results of the multivariate logistic regression analysis are credible.

New biofuels, as a sustainable energy alternative to traditional fossil fuels, are attracting global attention. With the increasing awareness of environmental protection and the continuous growth of energy demand, biofuels offer the possibility of reducing greenhouse gas emissions and decreasing dependence on fossil fuels. In this paper, by introducing the Wasserstein distance, which is used to describe the objective function of the GAN model, the self-attention mechanism is applied to improve the discriminator structure of the traditional WGAN-GP to achieve more efficient generation of high-quality data samples. The WGAN-GP model is used to design a new biofuel combustion scenario, and based on the combustion data, the new biofuel is prepared in the scenario. The final data generation results of the model are evaluated based on relevant evaluation indexes. It can be seen that the trend of the generated data set is consistent with the trend of the actual output value of the power station, and the interval range formed by the generated 50 sets of data can include the real data in a more complete way, with a high data coverage, and the error between the generated value and the real value is in the range of ±250-±300. The new biofuel output scenarios generated by the WGAN-GP model were utilized for EMF synthesis experiments. PTFE@ACMS-SO3H samples showed strong absorption peaks at 759cm-1 and 54cm-1 , indicating that the acidic groups-SO3H were successfully loaded on the surface of the material and the preparation of the novel biofuel was successful.

This study utilizes the Apriori algorithm for association rule mining, aiming to deeply explore the intrinsic connection between college students’ physical health and sports performance. The relevant definitions of association rule mining and the application process of Apriori algorithm in this study are elaborated in detail, including data preprocessing, frequent item set generation, and association rule extraction. Through empirical analysis, various combinations of physical fitness factors affecting college students’ athletic performance and the corresponding association rules are revealed. For example, under the condition of support degree of 0.598 and confidence level of 0.709, when male students’ “stiffness upward grade” is passing, their athletic performance is also passing. By mining the correlation rules between college students’ sports performance and physical health, it provides scientific basis and targeted suggestions for physical education and students’ health management in colleges and universities.

With the rapid development of virtual reality technology, its application in the field of art and design is attracting increasing attention. Based on the perspective of user demand, the article combines the Kano model to analyze user satisfaction with virtual reality technology used in modern Chinese image culture design, and finds that its landing point is the desired attribute in the first quadrant, with the Better and Worse coefficients of 0.531 and -0.141, respectively, which indicates that users expect the application of virtual reality technology in the design of image art and culture. Then the evaluation index system of VR image art and culture design is constructed, and the principal component analysis method is used to assign weights and establish the quantitative model of VR image art and culture design. The analysis shows that the weight of the sensory level is the largest 0.3780, and users attach great importance to the aesthetic experience (0.3780) and emotional experience (0.2710) of VR image art and culture design. The application of virtual reality can draw on the results of the quantitative model to design optimization strategies, combine traditional and modern elements, use the interactivity of VR to enhance artistic expression, create an immersive experience, and create more in-depth and original works of video art and culture.

In recent years, the construction of education informatization has been comprehensively promoted, and the personalized learning recommendation model has brought a new direction for the development of intelligent learning platform for college English vocabulary. This study constructs the KCPE-SR model based on collaborative filtering algorithm and knowledge graph, generates and optimizes the suitable personalized learning paths for learners through the interaction between learners of college English vocabulary and resources, and develops a personalized college English vocabulary learning system based on this model. The analysis of the application effect of the system reveals that the experimental class students’ English vocabulary learning performance has been significantly improved with the help of the personalized learning system, and the students’ English vocabulary knowledge mastery (20.00 points) and vocabulary comprehensive application ability (20.49 points) have also increased. The personalized college English vocabulary learning path generation and optimization system proposed in this paper is able to achieve accurate personalized recommendation of learning resources and can meet the needs of college English vocabulary learning.

In recent years, the scale of the electric vehicle industry and social ownership are gradually growing, in the case that the charging facilities are not yet able to meet the demand for electric vehicle charging. Aiming at the situation described above, the research of charging station siting supported by variable neighborhood genetic algorithm is proposed. Based on the principle of charging station siting, the objective function and constraints are set, and the design of charging station siting model is realized. It is found that the traditional genetic algorithm, which has the problem of poor search ability, adopts the variable neighborhood genetic algorithm to solve the model. Calculated, this paper’s algorithm in the charging demand peak period scenario, to determine the optimal charging station site selection there are four, the two objective function value of 0.94, 0.98, both in the charging peak period or the low peak period, this paper’s method compared to the traditional genetic algorithm has a higher superiority.

The rapid growth in the scale of cross-border data flow has pushed the protection of personal information to become an important issue of global concern. This paper drafts a legal adjustment mechanism for the protection of personal information under cross-border data, and builds a data sovereignty practice system from the aspects of comprehensive strength construction and cross-border flow pilot. It utilizes civil law, criminal law and administrative law to protect personal information in cross-border data flow. Based on the numerical analysis method, the legal protection mechanism of personal information in cross-border data flow is discussed in depth. The numerical analysis results show that the probability of personal information exposure increases to about 0.35 when the ratio of malicious nodes under the legal mechanism of this paper is 0.5. The estimated accuracy of personal information protection effect increases by 65.16% to 80.52% when the enforcement strength of this paper’s mechanism is 0.7 and the sample size of companies is 300. Fixing the initial ratio of cross-border data information disclosure, the smaller the initial ratio of personal information protection, the faster the speed of personal information leakage under the legal mechanism. The investigators’ scores on the personal information risk indicators of a cross-border e-commerce platform are uniformly distributed between 1 and 2, and the sum of the overall scores is less than 10, demonstrating the effectiveness of the legal mechanism constructed in this paper on the protection of personal information.