Research on Empirical Digital Modeling of Artificial Intelligence Technology to Predict Investment Potential of Automobile Industry

This paper presents an empirical digital model based on artificial intelligence technology to predict the investment potential of the automotive industry, with special attention to the impact of social network structure on investment decisions. Firstly, a comprehensive data set was constructed, covering multi-dimensional information such as economic indicators, consumer behavior, technological innovation, and social network relations of the automobile industry, which provided rich data support for the training and verification of the model. A deep learning framework combined with social network analysis techniques is used to develop a set of predictive models that can identify key nodes and relationships in complex network structures. The model not only considers the influence of traditional economic factors, but also deeply analyzes the potential role of social network structure on investment return, such as the influence of opinion leaders, the efficiency of information transmission paths, etc., which are often ignored in traditional models. Through the system simulation, the model can accurately predict the investment potential of the automobile industry. The simulation results show that the integration of social network structure significantly improves the accuracy and reliability of the prediction. In addition, the model also shows robustness in the face of market fluctuations and uncertainties, which can provide investors with more comprehensive and forward-looking decision-making basis.