Research on the Development of New Energy Electric Vehicles Based on Factor Analysis and Intervention Optimization of LSTM Model

Given the two-carbon strategy, the new energy vehicle industry is expanding rapidly. This paper studies the problems related to the development of new energy electric vehicles in China through modeling analysis. In this paper, the random forest algorithm is first used to evaluate the main factors affecting the development of new energy electric vehicles in China, and then the principal component analysis algorithm is used to determine the impact of each factor. The survey results show that the number of public charging stations, the number of state-supported policies and the level of population consumption are dominant; Then, we collected the development data of China's new energy electric vehicle industry, and used the Logistic algorithm to create an EV prediction model to predict the progress of China's new energy electric vehicles in the next ten years. Further, this paper collects data on electric vehicles powered by new energy sources and analyze their impact on the established global conventional energy vehicle industry. Using the VAR model, this study assesses possible correlations between the production and sales of new energy electric vehicles in China and globally. Subsequently, the functional equation of domestic and global joint production and sales of new energy electric vehicles is derived. The influence of policies restrict the development of new sustainable energy electric vehicles on the development of new sustainable energy electric vehicles in China is analyzed. First, the data that is not affected by the policy restriction, which is selected, and the rationality of the model is verified by LSTM algorithm. An intervention analysis model is introduced to optimize the LSTM model while considering the policy constraints. Through this optimized fitting process, the fitting results are consistent with the actual results, and the impact of policy restrictions on the development of new electric vehicles in China can be quantified.