Contribution Analysis of Regional Tax Factors Based on RBF Machine Learning

Abstract

Tax revenue is one of the necessary conditions for ensuring economic stability and development in market economy countries, which affects the rational allocation of social resources, and the influence of tax relationships has become a necessary research topic. The artificial neural network has been widely used in many fields as an effective method to deal with complex nonlinear problems. Based on the data on the local economy, population, environment, and policy in North China from 2011 to 2020, this paper analyzes the four factors affecting tax revenue, establishes an artificial neural network-radial basis function(ANN-RBF)model, and uses SPSS to calculate the importance of normalization of regional tax contribution in four aspects of the local economy, population, environment, and policy. This study has particular enlightening significance for the country to formulate and use tax policies for macro-control.