Achieving high-quality development has become the core essence of tourism industrialization, and is also a necessary step for the construction of ecological civilization to make new achievements. The article establishes the index system of China’s tourism high-quality development, and uses the entropy weight-TOPSIS model to measure the tourism high-quality development of China’s tourism in each region from 2013 to 2021. On this basis, it comprehensively applies density estimation, Dagum Gini coefficient and convergence modeling methods to analyze the regional differences and convergence of China’s tourism development. The study shows that the level of high-quality development of China’s tourism industry is gradually rising, and the regional differences in high-quality development of tourism are generally narrowing, with insignificant changes in intra-regional differences and narrowing of inter-regional differences, though. The overall trend of wave height in the central region is increasing, the wave height in the western region is decreasing and the width is increasing, and the wave height in the northeast region is increasing and the width range is decreasing. At the same time,  convergence coefficient shows that the gap between the level of high-quality development of tourism economy in the eastern, central and northeastern regions shows a trend of convergence, while the western region increases from 0.373 in 2012 to 0.388 in 2021, that there is no trend of convergence.

This paper synthesizes relevant theoretical knowledge and construction principles, selects 20 evaluation indicators to constitute the evaluation system, and divides the evaluation system into two subsystems in order to more intuitively demonstrate the relationship between international trade network optimization and regional economic synergy. Setting the source of research data, due to the initial data outline is not uniform, the research data for the dimensionless processing. Then the weight values of each index are calculated with the help of entropy weight method, and their values are substituted into the coupled synergy model of the fusion evolutionary algorithm. It is calculated that the synergy level of international trade network optimization and regional economy is medium in the period of 2014~2016, the coordination level of the two has been significantly improved in the period of 2017~2021, and the coordination level is good, and the coordination level of international trade network optimization and regional economy rises to excellent in the period of 2022~2023.

As an environmentally friendly and efficient public transport, the optimization of the operating frequency of electric buses is of great significance for improving passenger satisfaction and reducing operating costs. This paper proposes an optimal electric bus frequency setting method that combines LSTM prediction and two-layer planning. First, LSTM neural network is utilized to predict the passenger flow of electric buses. Second, a two-layer planning model is constructed, with the upper model aiming at frequency optimization and the lower model aiming at electric bus frequency setting. Finally, this two-layer planning model is solved by genetic algorithm to obtain the optimal electric bus frequency setting. The inbound and outbound passenger flow data of the 5th station of 363 electric bus in Q city are used for practical verification. The prediction results of the LSTM model on inbound and outbound passenger flow on weekdays and natural days are basically consistent with the actual values. The optimal frequency of 62 trips was solved using genetic algorithm. The maximum deviation of the actual capacity supply from the actual capacity demand curve is only 0.09% when the frequency setting is verified under the scenario of thousands of passenger flows. From the above analysis, it is shown that it is practical to design the optimal electric bus frequency using LSTM prediction and two layer planning model.

At present, drilling fluid leakage in oil and gas drilling engineering in complex formations is a worldwide technical problem. The study explains the mechanism of dense pressure-bearing plugging at the bottom of the fracture, explores the influencing factors of the pressure-bearing capacity of the leakage prevention and plugging working fluid, and establishes a mathematical model by using multivariate nonlinear regression analysis. Based on the machine learning technology, the support vector machine algorithm is selected as the prediction method of the particle size of the working fluid for leakage prevention and plugging, and the system model of the ultra-high-temperature dense pressurized leakage prevention and plugging working fluid is constructed. It is found that the established multivariate nonlinear regression analysis has good fit and accuracy, and the average relative error is only 2.9%, and the seam width (-0.694) and formation pressure (0.502) have the greatest influence on the pressure-bearing capacity of the working fluid for leakage prevention and plugging. The prediction accuracy of the support vector machine model for the working fluid particle size was 95.36%, and the prediction F1 values on multiple datasets were all greater than 0.9, showing excellent prediction results. The constructed mathematical model can be used to guide the field operation, which is conducive to the long-term stable plugging and scientific leakage prevention of fissure-based leakage.

Chinese oil painting art is an important carrier of contemporary Chinese cultural identity features, the identification and quantitative study of the color and texture of the picture can help to understand the characteristics of the oil painting works more deeply. Therefore, this paper proposes a feature recognition method for oil painting art based on deep learning method. The Otsu threshold method and DeeplabV3+ network model based on DeeplabV3+ are selected for image graying and segmentation processing. The global color histogram and ring LBP are used to extract the color and texture features of the picture respectively, and the oil painting feature recognition is completed based on the regularized limit learning machine. In several sets of quantitative results, the methods in this paper all have better oil painting color and texture feature recognition, among which the RELM algorithm has the highest detection accuracy at low correlation features. It shows that the deep learning based Chinese oil painting art and cultural identity feature recognition method can effectively extract oil painting features and realize the quantitative research on oil painting.

The article firstly establishes a mathematical model of the FMS shop floor planning process problem, and combines the rescheduling strategy and rolling scheduling strategy for solving the FJSP problem. Subsequently, the simulated annealing genetic algorithm is improved by relying on genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm, and the application of hybrid optimization algorithm in problem solving is proposed. The simulated annealing algorithm is incorporated into the crossover and mutation operations of the genetic algorithm to strengthen the local search capability, and then the global annealing operation is incorporated into the new individuals obtained. The overall design of the mixed reality-based FMS virtual simulation system was tested with a view to optimizing the external tool library tool limitation problem in the FMS shop floor planning process. The results of the simulation experiments show that although the algorithm of this paper, SaDE and CoDE algorithms can reach the optimal solution, the convergence speed of the algorithm proposed in this paper is significantly better than the other two algorithms. Based on the experimental results, the article finally constructs a mixed reality-based FMS virtual simulation system to solve the external tool library tool limitation problem in the FMS shop floor planning process.

Agent technology is widely used in intelligent manufacturing and digital workshop as a new method to solve complex, dynamic and distributed artiϐicial intelligence application problems. This paper ϐirstly summarizes the application steps of Agent technology in 3 aspects of modeling, simulation and monitoring of intelligent manufacturing system on the basis of a brief description of multi-agent system. Then, based on reinforcement learning theory, a multi-agent collaborative algorithm SRL_M3DDPG based on state representation learning is proposed.Finally, the algorithm model is tested and applied to the smart shop scheduling problem. The learning curve of the SRL_M3DDPG algorithm in the example remains relatively stable after the 3400th round, and the maximum completion time of the scheduling is 29. Comparing with other composite scheduling rules, the delay rate of this paper’s algorithmic model is the lowest, which is only 15.47%, which indicates that the algorithm is able to signiϐicantly reduce the delay rate of the workpiece. In addition, this paper’s algorithm achieves better results in adaptive intelligent manufacturing workshop scheduling, ϐinding the shortest machining completion time of 221 unit time, which can adapt to the dynamic intelligent manufacturing workshop environment.

This paper constructs a comprehensive evaluation system based on the CIPP model, covering multiple dimensions such as input evaluation and outcome evaluation, in order to comprehensively measure the effect of college students’ mental health education in the new media environment. In terms of weight determination, the subjective weights are obtained by hierarchical analysis method, then the objective weights of each index are calculated by entropy value method based on the actual data, and then the combination assignment method is used to organically combine the subjective and objective weights to obtain the ϐinal indexes. The relationship matrix was constructed on the basis of a large amount of collected data, and the fuzzy comprehensive evaluation method was used to comprehensively assess the implementation effect of college students’ mental health education. The results of the study show that the overall level of the effect of college students’ mental health education is good, with the ratings of 79.54 and 78.28 for their mental health knowledge and ideological awareness evaluation, respectively, and that the mastery of mental health methodology and the awareness of proactively seeking psychological help are the main factors affecting the mental health of college students. In addition, the mastery level of college students’ mental health practice ability is average (69.52), and there is an obvious deϐiciency in their theory to practice, which also adds difϐiculty to the construction of college students’ mental health. Therefore, the fuzzy comprehensive evaluation method can be used to optimize the evaluation system of college students’ mental health education in the new media environment.

With the rapid development of artificial intelligence technology, the research on personalized learning in the field of ideological and political intelligence education is increasingly active. In this paper, an improved locust optimization algorithm is proposed, which is applied to the intelligent grouping strategy of ideological and political education. Then a knowledge state-oriented hypergraph self attention knowledge tracking model is proposed, which consists of a hypergraph module and a self attention module, and is capable of predicting students’ future interaction sequences through their past interaction sequences. In order to realize students’ personalized test question matching needs, a Civics test question recommendation algorithm based on the neural graph model is proposed, based on which a personalized Civics test question recommendation exam system is designed and implemented. The intelligent grouping strategy based on the optimized locust algorithm achieves a total score accuracy of 100% in the Civics grouping task. The knowledge tracking model accurately predicts students’ knowledge status, and the attention weights of students’ learning paths based on this paper’s recommendation algorithm are all higher than 0.5. It shows the effectiveness of this paper’s strategy of automatic generation of Civics education content based on the locust optimization algorithm and the personalized test question matching model on the students’ in-depth understanding of the Civics knowledge and improvement of learning efficiency.

In the face of the traditional agricultural marketing model is difficult to continue the status quo, agricultural marketing competition – cooperation relationship for agricultural enterprises of commodity marketing and long-term development is also increasingly important. In this paper, game theory is introduced into the study of competition and cooperation strategy of agricultural products marketing, the strategic behavior of two agricultural products enterprises in the agricultural products industry cluster is constructed into the corresponding matrix, and evolutionary dynamic stability analysis is carried out to establish the replication dynamic equations and Jacobi matrix to solve the evolutionary stability strategy (ESS), so as to provide reference for the formulation of the competition and cooperation strategy of the enterprise’s agricultural products marketing. Using simulation to explore the influencing factors of the evolutionary direction of the marketing competition and cooperation strategy of agricultural products enterprises. When the probability of winning the joint bidding is greater than 0.8, it will evolve into a cooperative strategy, and when it is less than 0.7, it will evolve into a competitive strategy, and with the increase of the allocation coefficient of the investment amount of the project construction, the agricultural products enterprise 1 and the agricultural products enterprise 2 will gradually shift from a competitive strategy to a cooperative strategy. The lower the cost allocation coefficient is, the higher the probability that enterprises will evolve to cooperative strategy. The increase of cooperative transaction cost then accelerates the evolution of enterprise 1 and enterprise 2 to competitive state.