Supported by the theory of economic growth convergence, this paper takes the eastern, central and western regions as the research object during 2010-2020, analyzes the economic growth convergence of the eastern, central and western regions of the country, and verifies the relationship between the regulation of fiscal policy and the high-quality development of the regional economy. Analyze the relationship between regional economic development, fiscal policy and economic convergence, and put forward the analytical view that fiscal policy affects regional economic convergence. The combination of dynamic panel model and absolute  convergence analysis is used to derive the results of the absolute convergence test of regional economic growth. Convergence role test for fiscal expenditure variables, transfer payment variables. It is brought to the western region to analyze the role of government expenditure in the western development policy on the convergence of the western region’s economy. Convergence  as well as absolute  convergence is conducted for each of the eight comprehensive economic zones, and the regional economic high-quality development policies are adjusted. Relative to 2010-2015, there is no convergence in economic growth in the western region in 2016-2020, and there is a tendency to divergence, which suggests that the fiscal policy of western development has limited effect on economic convergence among regional provinces. Absolute  convergence exists for the whole country and the eight comprehensive economic zones, and the convergence coefficient is significantly negative at the 1% level. However, the speed of convergence varies for high-quality economic development.

Currently, the development of cultural tourism has become a new trend of urban development, and how to use modern technology to realize the innovative development of urban cultural tourism has become a key issue to be considered in the process of urban construction. The research combines the Web domain ontology to construct a multi-level user portrait master model, which mainly includes four sub-models: retailer static attribute vector model, retailer domain dimension model, retailer marketing ability model and retailer social dimension model. The FCM algorithm based on the improved AP algorithm is utilized to cluster the user portraits, and the user portrait clusters obtained by the method studied in this paper perform well with an average number of iterations and an average time consumed of 21.3 and 60.35 compared with the traditional K-Means algorithm, the improved KMeans algorithm, and the traditional FCM algorithm, respectively. Then a personalized recommendation method for tourism products based on MAGFM is proposed, which achieves Top-N recommendation of tourism products by calculating the total interest value of users and the comprehensive similarity of tourism products. And test and analyze in the tourism e-commerce platform, the results show that the recommendation algorithm proposed in this paper has higher effectiveness compared with the traditional recommendation algorithm. Finally, the research content builds a personalized recommendation system for tourism cultural and creative products.

Energy level fluctuations in Distributed Generation (DG) systems and Electric Vehicles (EVs) sometimes exceed the carrying capacity of typical distribution network topologies, which may lead to inefficiencies and lack of reliability. Based on this, this paper introduces a new Levy flight-electric eel foraging optimization (LF-EEFO) method for adapting network topology reconfiguration for new power systems. The DG output power, EV charging power, distribution network loss power, and switch lifetime cost cost are taken as the objectives, and the tidal current, voltage, branch power, network topology, and switching action are set as the constraints, in order to construct a multi-objective optimization model for distribution network topology reconfiguration. In the optimization phase, a Levy flight strategy is used to optimize the local search capability of the EEFO algorithm to obtain the optimal solution of the multi-objective optimization model for distribution network topology reconfiguration. In order to ensure the efficiency of the LF-EFO algorithm in optimizing the distribution network topology reconstruction model, an IEEE-33 node test system was established for simulation analysis. The results show that this research can significantly reduce the operating cost and improve the operational reliability of distribution networks, while promoting the development of electric vehicles.

Music therapy is the treatment of college students’ psychology through various techniques and methods of music, and this paper focuses on researching and analyzing the improvement effect of music therapy on college students’ mental health in the context of cultural education. Students’ physiological data are collected and denoised, and machine learning models are used to realize the multimodal fusion of all kinds of physiological signal features to obtain the objective psychological state assessment values of college students. The subjective assessment results of the mental health assessment scales were then combined to analyze the improvement effect of mental health of college students in the music therapy intervention. The analysis of the psychological health status of the students before and after the intervention experiment revealed that the objective assessment values of the psychological state of the college students in the intervention group gradually tended to be positive with the music therapy, and the subjective assessment results of the psychological health scales of the students in the intervention group were signiϐicantly better than those of the nonintervention group after the experiment (P<0.05). Music therapy has a signiϐicant role in intervening in the mental health of college students and resolving their psychological malaise, which is of great practical and guiding signiϐicance in improving the psychological tolerance and health of college students.

For national grid power line infrastructure construction construction, quality management and control can ensure improved safety standards, long-term reliability and cost savings through avoiding rework. In this paper, a high-definition image of a transmission line is collected from multiple viewpoints by a UAV, and a model for recognizing surface defects on infrastructure lines is proposed to reduce the computational complexity to improve the YOLOv8 algorithm. The model uses ResNet50 as the feature extraction backbone network and fuses convolution and attention mechanisms to enhance global and local feature extraction. A multi-scale feature aggregation diffusion module is added to the neck network of the model to enhance the detection of small targets on infrastructure lines. Finally, the classification loss function combined with the PIOU bounding box loss function is introduced to further enhance the recognition accuracy of infrastructure line surface defects. The experimental results show that the mAP of the infrastructure line surface defect recognition model is up to 0.935, which is 2.41% higher than that of the baseline model, and the performance is significantly better than that of some of the current mainstream defect recognition models. Therefore, from the computational complexity, combined with the target detection YOLOv8 algorithm can realize the accurate recognition of surface defects on infrastructure lines, and provide reliable data support for improving the timely repair of grid infrastructure lines.

In recent years, with the development of science and technology, image enhancement has become a very important topic in scientific research, become an indispensable part of machine vision, and has a wide range of applications in various fields of computer vision. In this paper, the image gradient enhancement algorithm is first improved based on the image gradient field, and its enhancement effect on low quality (low resolution) images is found to be poor through experiments. For this reason, the study constructs a multi-scale feature image enhancement model (LIEN-MFC) by convolutional neural network to further optimize the image enhancement effect. By comparing with different algorithms, the average PSNR of the model is 21.80 and the average SSIM is 0.8767, and it outperforms other compared algorithms in both PSNR and SSIM. In addition, the ablation experiments demonstrate that the enhancement effect of the LIEN-MFC model is further improved on the basis of the improved image gradient enhancement algorithm. The results show that the image enhancement model algorithm with multi-scale features proposed in this paper has a significant image enhancement effect and the improved image gradient enhancement in image enhancement of convolutional neural networks improves the model performance to some extent.

The expansion of English vocabulary is the foundation of college students’ English learning and the key to improve English learning. This project centers on the quantitative analysis of college English vocabulary learning efϐiciency improvement, through the questionnaire survey to understand the use of English vocabulary learning strategies of students. The inϐluencing factors of English vocabulary learning efϐiciency improvement are selected, correlation analysis is carried out, and then multiple regression model is used to explore the role of each variable on the improvement of English vocabulary learning efϐiciency. The results show that students most often use the metacognitive strategy of preplanning (3.674), and that students who are good at learning are more inclined to adopt the metacognitive strategy to control vocabulary learning from a macro perspective. Multiple selfelements and environmental elements together positively affect the improvement of English vocabulary learning efϐiciency (p < 0.01), with the most signiϐicant effects of learning strategies (0.482), teaching methods (0.457) and learning strategies (0.416). It is recommended to promote the efϐiciency of English vocabulary learning through innovative teaching methods, combining word class memorization, expanding the scope of reading, and vocabulary association learning.

In order to better realize the automatic classification and change detection of remote sensing images, this paper proposes an automatic remote sensing image classification model based on CNN and migration learning, and constructs a remote sensing image change detection model by combining CNN and Transformer network. In the remote sensing image classification model, DenseNet network and Inception network are used as the backbone network, combined with the new channel attention module to mine the image features of remote sensing images, and then realize the accurate classification of remote sensing images. In the remote sensing image change detection model, the convolution operation of CNN with different sizes of void rate and expansion rate is utilized to better guide the feature map to focus on local information. Combined with the dynamic deformable Transformer to provide more accurate remote sensing image location information and detail information, to reduce the impact of background interference on remote sensing image change detection, and to improve the model’s ability to recognize the target of remote sensing images. The parameter count and floating-point computation of the remote sensing image classification model are 7.69MB and 1.89GB, respectively, which are smaller than the parameter count and floating-point computation of the single network model. The RSICD models mF1 and mIoU are 1.66% and 0.58% higher than the optimal ones. Through the effective integration of convolutional neural networks and many different types of deep learning techniques, automated classification and change detection of remote sensing images can be realized.

The development of globalization has contributed to the increasing demand for cross-language communication, and machine translation, as an effective language conversion tool, has improved the quality and efficiency of English translation. The article discusses the syntactic optimization and semantic reconstruction strategies for English translation based on machine learning. The machine translation model of English syntax optimization and semantic reconstruction based on EM algorithm is constructed by using key technologies such as EM algorithm and multi-head attention mechanism. The model adopts a joint learning method, combining the Transformer model with the EM algorithm. The dependency between any two words in the input sequence is captured using the multi-head attention mechanism, and the new translation corpus is generated by multi-task joint training algorithm. The training phase of this paper’s model has good translation effect, and the model of this paper gets the highest BLEU score of 32.86 when the number of multi-head attention layers is 1. The distribution of semantic features of translation reconstruction under this paper’s method is basically consistent with the simulation results, and the error elimination rate of semantic reconstruction is 99.64% when the number of samples is 500. The method in this paper is more effective in syntactic structure optimization, with the highest BLEU scores on “Chinese to English” and “English to Chinese”, and the syntactic correctness rate on English long sentences of different topics reaches 88.69%~96.57%.

This topic is centered around temperature and stress, and describes the theory of electric power thermal characteristics. There are usually two methods for thermal coupling analysis, for direct coupling and sequential coupling. Considering that the stress field of the cable does not have much influence on the temperature field, it is proposed to use the sequential coupling method for the calculation of the thermal characteristics of the cable. The calculated and solved cable temperature and stress distribution values are put into the Lap-ML-ELM algorithm for training. When the contact coefficient k=1, 4, 7, 10, 13 and 15, the cable joints and surfaces produce a monotonically increasing law of temperature, and the stress exhibits the same situation.During the training of the model on the thermal characteristics of the cables, it is found that the accuracy curve of the thermal characteristics detection of the Lap-ML-ELM algorithm is higher than that of both the RNN network and the CNN network, which shows that in the detection of the thermal characteristics of cables, the Laplace Multilayer Extreme Learning Machine fusion algorithm performs more obviously.