Construction of Enterprise Legal Risk Prevention and Control System Based on the Rule of Law Environment

Abstract

Due to the growth of economy, the increasing competition in the reform of enterprise system and the emergence of legal constraints, it is urgent to raise the awareness of legal management in the management activities of enterprises. While facing opportunities, modern enterprises also face many challenges and risks. In the whole process of enterprise management, due to internal and external environmental factors, enterprises may face different legal risks all the time. In this article, the current situation of enterprise legal risk is analyzed, and the back propagation neural network (BPNN) is used to build an enterprise legal risk assessment model, which enriches the basic theory of legal risk assessment and improves the risk prevention and resolution ability and management efficiency of modern enterprises. Noise reduction is added to the algorithm, which optimizes the robustness of data features and improves the data generalization ability of BPNN model. After many iterations, the accuracy of this method is obviously better than that of the traditional BPNN model, with an accuracy of over 96% and an error reduction of 33.64%. Applying the legal risk assessment model based on BPNN can effectively shorten the response time of the system and reduce the incidence of legal risks in enterprises.