Analysis of the Influencing Factors Affecting the Satisfaction of New Energy Vehicles Based on CatBoost Model

In order to solve the problem that automobile enterprises want to be able to keep track of the satisfaction of a large number of consumers with new energy vehicle products at all times, this paper proposes a scheme based on the CatBoost machine learning model to analyze the influencing factors of the satisfaction of new energy vehicle products. This scheme first uses market research methods and SPSS software to obtain the relevant original data; Next, the box plot is used to find outliers, the R language function is used to find missing values, and the missing values are filled with mean and mode respectively according to the characteristics of the dimension data, and then the Min-Max method is used to standardize the data. Finally, the principle of the CatBoost model is introduced; The results show that range, safety and economy are the indicators that consumers are more concerned about, and the results meet the actual requirements. Based on the research conclusions of this paper, it can provide reference and basis for improving product satisfaction and precision marketing of enterprises.