Application of T-S neural network and coupling coordination degree model in the research of development trend of new energy automobile industry

This paper studies the key factors affecting the development of new energy automobile industry, and establishes a comprehensive index system. Through the random forest method, high-impact indicators were selected, and six factors affecting the development of new energy vehicles in China were finally determined. Spearman correlation coefficient method was used to quantitatively evaluate the impact of different indicators on new energy vehicle sales, and An industry development index evaluation model based on T-S fuzzy neural network and Fourier transform is constructed to evaluate the future development trend of China’s new energy automobile industry. Then, the ARIMA model is used to predict the development trajectory of China’s new energy vehicles in the next ten years. Finally, a dual-system coupling model is constructed to quantitatively study the coordinated development relationship between the global new energy automobile industry and the traditional automobile industry system.