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.

County economic development directly affects the national economy, and the county economy of Henan Province has become the economic pillar of the province. The purpose of this paper is to analyze the county-level economic development of Henan Province and its economic influencing factors by using the quantitative evaluation method. From the time series, the level of economic development of 105 county units in Henan Province from 2000-2023 is analyzed from two perspectives, absolute difference and relative difference, using the indicator of GDP per capita. Screening of factors affecting the level of economic development of counties in Henan Province is carried out from the aspects of population, resources, policies, etc., and a four-aspect indicator system is constructed, namely, human capital, government regulation, industrial level, and economic vitality. A multiple linear regression model is established, and the regression model is fitted by the regression coefficients of each influencing factor, and the fit of the regression model is examined. Each county in Henan Province is divided into three development gradients: developed, generally developed and less developed counties. Panel data regression analyses were conducted on the overall county economy of Henan Province and the influencing factors of developed, generally developed and less developed counties respectively. In the overall economic development of counties in Henan Province, the degree of influence of physical capital investment and the structure of secondary and tertiary industries on the overall differences in county economies is particularly significant. It is manifested in the fact that for every 1% increase in the investment in fixed capital of the whole society, the output of GDP per capita increases by 0.09112% accordingly. Therefore, in order to improve the differences in the economic development of counties in Henan Province, local governments and enterprises should make efforts to improve the market and investment environment and adjust the structure of secondary and tertiary industries.

The construction of harmonious labor relations is of great significance in improving the quality of public services and promoting social harmony and stability. The study uses multi-period DID algorithm to construct a mathematical model of artificial intelligence application and labor dispute resolution, and conducts research on the influence relationship between the two. Aiming at the lack of preventive mechanisms for labor dispute resolution at present, principal component analysis and artificial neural network are used to establish a labor relations early warning model. The results show that artificial intelligence application has a significant positive impact on labor dispute resolution at the 5% level, and there is regional heterogeneity.The prediction accuracy of PCA-ANN model on labor relations in the training set and test set is 81.25% and 85.71%, respectively, which presents a good effect of early warning of labor relations, and it can be used to improve the mechanism of labor dispute resolution. Finally, based on artificial intelligence technology, the online labor dispute resolution mechanism is proposed to prevent the escalation of labor disputes and improve the effectiveness of labor dispute resolution by focusing on prevention, secondary control and subsequent resolution.

Data empowers educational evaluation, and blockchain technology aids in the governance of educational evaluation data. The union of big data and blockchain technology has prompted the development of educational evaluation toward digitalization and precision of educational evaluation. This paper combines the multifaceted governance utility of blockchain technology for educational evaluation data and proposes to improve the consensus mechanism in educational evaluation information sharing. The PBFT consensus algorithm is updated with node contribution reward and punishment mechanism, the consensus nodes are selected by Fibonacci function characteristics, and the consistency protocol is optimized, so as to design a practical Byzantine fault-tolerant algorithm NCG-PBFT based on node contribution grouping, and analyze the credit value, throughput, normal block out delay, and the number of communications of NCG-PBFT consensus algorithm. Build a comprehensive education quality evaluation platform and bring in the improved PBFT consensus algorithm to test the operation performance of the comprehensive education quality evaluation platform. When the request frequency tends to be stable, the education comprehensive evaluation system of NCG-PBFT consensus algorithm is able to improve the system throughput by 74.54% compared with the PBFT algorithm, which is able to meet the performance and stability requirements of the education comprehensive quality evaluation system.

The development of Artificial Intelligence has renewed the direction of art history, making the relationship between technology and art a matter of great interest once again. The application of artificial intelligence in the field of fashion design brings new tools to the designers’ way of designing and displaying. This paper researches artificial intelligence technology and analyzes the application of artificial intelligence as an auxiliary means in the field of art and design, and deeply researches the way of applying artificial intelligence in fashion design as well as its advantages. It also researches the intelligent image generation problem under the fashion big data environment, adopts the method of fusing the external features of fashion images and decoupling the internal features, and provides theoretical methods and bases for the controllable generation of fashion images based on the architecture of generative adversarial network. A multiconditional information fusion generative adversarial network architecture (MCF-GAN) is proposed, and the experimental results show that the image generation performance of the model in this paper is excellent, and better performance is obtained compared with other comparative methods. And it is applied to the actual fashion design for evaluation, the designer’s evaluation in all dimensions are more than 10 points, indicating that the method in this paper has a better application value in fashion design, and provides an effective path for fashion design optimization.

Aiming at the traditional pavement construction, there are problems such as poor construction conditions, limited quality inspection methods, backward control mode and incomplete management means. In this environment, the project in this paper (Gansu Road and Bridge Anlin Pavement Second Standard Project) uses multi-objective particle swarm optimization algorithm to establish a multi-objective machine group optimization configuration model based on quality constraints under the schedule – cost, and the first time to quote asphalt pavement to carry out the intelligent construction of unmanned machine group in Gansu Province. Analyze the intelligent unmanned machine group composed of auto-pilot paving technology and roller auto-pilot technology. Design the optimal configuration model of highway construction machine group, and use multi-objective particle swarm algorithm to design the cooperative operation of unmanned machine group. Combined with the optimal configuration of highway construction fleet problem itself, the standard particle swarm algorithm and fleet configuration model are also modified and improved. Simulate the highway pavement construction process, emphasizing the preparation of construction personnel, machinery, and management platform. The parameters of particle swarm algorithm are designed to solve the optimal construction machine fleet optimization configuration under quality constraints of duration-cost. The machine utilization and duration of scheme 2 are 15.23% and 10.96%, respectively. With the priority of duration, scheme 2 is selected as the machine fleet configuration scheme. Option 4 has the lowest machinery cost of 9.41%. With the priority to ensure the maximum profit, option 4 can be chosen as the machine swarm configuration scheme.

This paper combines the development situation of blue carbon industry to formulate the multi-dimensional optimization model construction of blue carbon industry cluster path. First set the model decision variables and objective function, and divide the constraints. Select the genetic algorithm to solve the optimization model. Determine the research data sources and genetic algorithm parameters, and analyze the multidimensional optimization model. The sensitivity coefficients of each decision variable to the optimization model are 0.2~0.1, and its sensitivity level is III, which means that the selected decision variables meet the research requirements. Compared with the other three algorithms, this paper’s genetic algorithm has superiority in four performance indicators, indicating that the genetic algorithm is more suitable for optimization model solving, and finally, the optimization model of this paper is put into the actual blue carbon industry, and it is found that there is a significant difference in the effect of carbon reduction, economic gain, green environmental protection, and satisfaction before and after the optimization (P<0.05), which verifies the effectiveness of this paper's optimization for practical application, and finally, according to the optimization results, the Finally, according to the optimization results, the corresponding optimization path is proposed.

Although China’s research on English is not as early as that of the western countries, researchers, combining the basic national conditions of China and the actual situation of the nationals’ learning of English, have been making continuous efforts in the research on the construction and application of English corpus, and have already achieved satisfactory results. In this paper, we first analyze the related contents of English corpus, and construct English corpus corpus from phonological and semantic aspects by analyzing the correlation characteristics between English corpus and semantics, according to the basic principles of corpus selection. Combining two word vector similarity measures, Jaccard similarity and edit distance, finally constitutes the final similarity calculation algorithm for English sentences. The MECNC model is constructed by integrating the joint representation and co-representation learning methods, and using edge probability to abstract the connection between two nodes. Experimentally analyze the word vector similarity of English corpus with the results of English corpus recommendation based on multilayer network representation. The correlation scores of Jaccard similarity metric in WS-SIM, WS-REL, MEN, Mtruk-771, and Simverb-3500 are 0.8069, 0.6668, 0.7389, 0.7125, respectively, 0.2769, which achieves the best results, so Jaccard captures more of the correlation between words. Experiments on link prediction task were conducted on five corpora using 3, 5, 8, and 10-fold cross-validation methods, and on the corpus CKM [245,1550], MECNC model OM3 has a maximum AUC value close to 0.94 at a cross-validation number of 8, which shows that MECNC, which is used as a guiding information for intra-layer wandering, shows a better performance.