Multi-User Automobile Internationalization Platform Construction Based on Big Data

This paper proposes a strategy to build an international automobile platform based on big data, aiming to provide personalized and efficient automobile trading experience for multiple users. This study first constructed a comprehensive automobile transaction model, which introduced the concept of utility value and quantified users' multi-dimensional demands for vehicle performance, price, brand preference, etc., laying a data foundation for the platform to provide accurate recommendation services. Aiming at the multi-objective optimization problem in the model, this paper designs and applies the genetic algorithm, which can efficiently explore the solution space and find the optimal transaction configuration to meet the needs of multi-users. The algorithm design section introduces the implementation process of genetic algorithm in detail, including key steps such as coding, selection, crossover, and mutation, as well as the calculation method of utility value, which ensures the flexibility and efficiency of the platform when dealing with multi-user scenarios. Through model simulation, this paper compares and analyzes the performance of the platform in terms of user satisfaction, transaction efficiency and cost control before and after adopting genetic algorithm optimization. Simulation results show that when processing transaction requests from 1000 virtual users, the optimized platform can reduce transaction matching time by 30%, increase user satisfaction by 25% on average, and reduce transaction costs by 20%. These significant improvements validate the effectiveness of genetic algorithms in improving the performance of automotive internationalization platforms.