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<p>At a time when the global wave of informatization and rural revitalization strategies are intertwined, smart rural construction, youth innovation and entrepreneurship have become the core driving forces to promote the modernization of agriculture and rural areas. However, there are obvious shortcomings in the data prediction and decision support of smart rural construction, especially in the ability to accurately model and efficiently process complex rural environmental data. Based on this background, this study actively explores the application potential of Long short-term memory network (LSTM) and simulated annealing algorithm in the construction of smart villages and the optimization of college students’ skill entrepreneurship paths. According to the actual needs of smart rural precision agriculture, the LSTM algorithm is used to predict key indicators such as crop growth cycle, probability of occurrence of pests and diseases, and price fluctuations of agricultural products in the market. The experimental results show that the accuracy of the LSTM model in crop yield prediction is as high as 92%, and the accuracy rate of price prediction is 95%, which is far beyond traditional statistical methods, and can provide farmers with effective market intelligence in a timely manner to help farmers increase production and income. At the same time, in order to improve the success rate and profitability of college students’ skill entrepreneurship projects, a series of simulation experiments were carried out with the help of simulated annealing algorithm, and an optimal path taking into account costs and benefits was determined through the simulation and comparison of different entrepreneurial models. Under this path, the initial return on investment of entrepreneurial projects can reach 35%, which is significantly higher than the industry average, providing clear guidance for young entrepreneurs. In addition, the LSTM is suitable for processing time series data and can effectively analyze the development and changes of smart rural construction, while the simulated annealing algorithm can efficiently find a near-optimal solution to optimize the entrepreneurial path. Based on these two algorithms, this study enriches the knowledge in the field of smart village construction and college students’ skill entrepreneurship, and is of great significance in the planning and decision-making of smart village construction and the ideas and methods of college students’ entrepreneurship.</p>

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Smart village construction and college students’ skills entrepreneurship path based on LSTM and simulated annealing algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月18日

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  • Xueli Dong

摘要

At a time when the global wave of informatization and rural revitalization strategies are intertwined, smart rural construction, youth innovation and entrepreneurship have become the core driving forces to promote the modernization of agriculture and rural areas. However, there are obvious shortcomings in the data prediction and decision support of smart rural construction, especially in the ability to accurately model and efficiently process complex rural environmental data. Based on this background, this study actively explores the application potential of Long short-term memory network (LSTM) and simulated annealing algorithm in the construction of smart villages and the optimization of college students’ skill entrepreneurship paths. According to the actual needs of smart rural precision agriculture, the LSTM algorithm is used to predict key indicators such as crop growth cycle, probability of occurrence of pests and diseases, and price fluctuations of agricultural products in the market. The experimental results show that the accuracy of the LSTM model in crop yield prediction is as high as 92%, and the accuracy rate of price prediction is 95%, which is far beyond traditional statistical methods, and can provide farmers with effective market intelligence in a timely manner to help farmers increase production and income. At the same time, in order to improve the success rate and profitability of college students’ skill entrepreneurship projects, a series of simulation experiments were carried out with the help of simulated annealing algorithm, and an optimal path taking into account costs and benefits was determined through the simulation and comparison of different entrepreneurial models. Under this path, the initial return on investment of entrepreneurial projects can reach 35%, which is significantly higher than the industry average, providing clear guidance for young entrepreneurs. In addition, the LSTM is suitable for processing time series data and can effectively analyze the development and changes of smart rural construction, while the simulated annealing algorithm can efficiently find a near-optimal solution to optimize the entrepreneurial path. Based on these two algorithms, this study enriches the knowledge in the field of smart village construction and college students’ skill entrepreneurship, and is of great significance in the planning and decision-making of smart village construction and the ideas and methods of college students’ entrepreneurship.