Research on the impact of entrepreneurship policy on employment based on improved machine learning algorithms

Abstract

After my country’s economy has entered a new normal, in terms of employment, which has led to the coexistence of the old and new contradictions in employment in our country and the coexistence of employment expansion and stabilization of employment. In this context, it is impossible to achieve full employment and completely eliminate unemployment by relying solely on economic growth. This paper improves traditional machine learning algorithms and builds an entrepreneurial policy analysis model based on improved machine learning to analyze the impact of entrepreneurial policies on employment. Moreover, this paper uses a projection pursuit comprehensive evaluation model optimized by genetic algorithm to conduct empirical research on entrepreneurial environment conditions. In addition, this paper verifies its rationality by regression analysis of empirical results and TEA (Entrepreneurial Activity of All Employees) index, and deeply explores the inherent laws and development characteristics of entrepreneurial environmental conditions from multiple perspectives such as time series and spatial distribution. The research results show that the method proposed in this paper is effective.