Research on the Prediction Method of Rural Industry Integration Based on Improved RBF Neural Network Model

The integration development of rural industries can promote the high-quality development of rural commerce, cultural industry and tourism. In this paper, we propose an improved RBF neural network-based rural industry integration prediction method to address the current problem of insufficient accuracy of rural industry integration prediction. Firstly, we use the entropy value method to obtain the influencing factors indexes of rural industry integration, and then use the RBF neural network as the basic prediction model. On the premise that the prediction results of RBF neural network are greatly influenced by the network parameters, this paper innovatively adopts the artificial fish swarm algorithm improved by Lévy flight to optimize the RBF parameters, thus finally obtaining the prediction model of rural industry integration based on the improved RBF neural network. Finally, the integration degree evaluation indexes obtained by entropy weighting method are input into the prediction model for experiments. The experimental results show that the rural industry integration prediction method proposed in this paper can predict the rural industry integration degree more accurately and has better computing efficiency, which is helpful for the study of digital transformation of rural industry in the context of digital economy.