Township and rural migrant workers are returning to the business personnel’s main force. Party 18 proposed a rural revitalization strategy, and the central committee of the State Council issued a series of encouraging policy measures to bring them back. The study found that migrant workers’ return-home entrepreneurship and the connection between the rural industries on the basis of factor resources flow are related. As a result, this study analyzed the practical situation of migrant workers’ return-home entrepreneurship. Next, it used a combination forecast method and dynamic simulation model of the migrant workers’ return-home entrepreneurship and revitalization of the relationship with the rural industry. The resuscitation of rural industries can be efficiently facilitated by the return of rural migrant workers to launch firms. Their success can attract more migrant workers to launch businesses. The total regeneration of rural areas and the prosperity of farmers can be achieved by effectively linking the return of migrant workers to launch businesses and the revival of rural industries.
Commercial banks are of great value to social and economic development. Therefore, how to accurately evaluate their credit risk and establish a credit risk prevention system has important theoretical and practical significance. This paper combines BP neural network with a mutation genetic algorithm, focuses on the credit risk assessment of commercial banks, applies neural network as the main modeling tool of the credit risk assessment of commercial banks, and uses the mutation genetic algorithm to optimize the main parameter combination of neural network, so as to give better play to the efficiency of neural network. After verification of various evaluation models, the accuracy of the evaluation model designed in this paper is more than 65%, while the acceptability of the evaluation results optimized by the mutation genetic algorithm is more than 85%. Compared with the accuracy of about 50% of the traditional credit scoring method, the accuracy of the credit risk evaluation using neural network technology is improved by more than 10%. It is proved that the performance of the optimized algorithm is better than that of the traditional neural network algorithm. It has important theoretical and practical significance for the establishment of the credit risk prevention system of commercial banks.
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