Construction and Research of Automotive Internationalization Platform Based on Spark Distributed System

This paper combines Spark's distributed computing capabilities with CNN's image recognition advantages to improve the transaction efficiency and user experience of the platform. First, this paper uses Spark distributed system to build a platform capable of processing large-scale auto transaction data. Spark's in-memory computing and task scheduling mechanism greatly improves the data processing speed and ensures the real-time and accuracy of transactions. Secondly, by improving the CNN algorithm, the intelligent recognition and classification of car pictures are realized, which not only optimizes the user search experience, but also provides the platform with accurate vehicle information matching capability. Finally, through detailed system simulation, the proposed scheme is verified to improve data processing performance, optimize transaction flow, and enhance user experience. The research results of this paper not only provide a new technical perspective for the construction of the automobile international platform, but also open a new way for the intelligent development of the automobile trading market. Through the fusion application of Spark and CNN, it shows the great potential of distributed computing and deep learning in solving complex transaction scenarios, and provides a valuable reference for the upgrading and optimization of future automotive trading platforms.