Evaluation of rural financial ecological environment based on machine learning and improved neural network

In order to improve the effect of rural financial ecological environment evaluation, this paper combines machine learning and improved neural network algorithms to construct a rural financial ecological environment evaluation system. First, this paper optimizes the input layer structure and its initial weight random assignment. The input layer structure is processed by factor analysis, and the initial weight random assignment is optimized by particle swarm optimization. Secondly, this paper constructs a rural financial ecological environment evaluation model based on factor analysis method and PSO-SOM to avoid the defects of traditional SOM algorithm used in financial ecological environment evaluation research. Finally, this paper constructs a system framework based on actual needs and designs experiments to verify the performance of the evaluation system constructed in this paper. The research results show that the system constructed in this paper meets the basic needs of rural financial ecological environment evaluation.

Automated summary

This paper constructs a rural financial ecological environment evaluation system by combining machine learning and improved neural network algorithms. Through optimizing input layer structure, weight assignment, factor analysis, and particle swarm optimization, the system successfully avoids traditional algorithm defects and meets basic needs.