Research on Algorithm for Assessing Automobile Market Competitiveness Based on Big Data Analysis

This study proposes a market competitiveness evaluation algorithm based on big data analysis and combined with game theory model. The algorithm conducts in-depth analysis of enterprise historical data, industry trends and competitor behavior patterns, and constructs a dynamic game model containing multiple variables to evaluate the competitiveness of different enterprises in the market. To ensure the accuracy and practicality of the algorithm, this study designed an algorithm based on multidimensional data sets and simulated the model in different market scenarios. In the simulation process, real market data and simulated data sets were used to comprehensively analyze the impact of various market strategies on competitiveness. The effectiveness of the algorithm was evaluated by comparing the game results of different enterprises in competition. The simulation results show that the competitiveness evaluation algorithm based on big data analysis can accurately predict the performance of enterprises in different competitive environments, and can adjust the evaluation strategy in real time according to market changes. The experimental results show that the algorithm can effectively improve the accuracy and efficiency of market competitiveness evaluation, and provide an important decision-making support tool for enterprises.