Design of Cross-Border E-Commerce Data Acquisition System Based on Python Crawler Technology

This paper proposes a Python based Xpath topic extraction method and an automatic identification method of cross-border e-commerce transaction data under BERT mode. This project intends to use BERT model to evaluate the correlation degree of topic content in the network. The ant algorithm based on pseudorandom criterion is used to calculate the probability of the mobile network of each agent. The pheromone concentration of each route is updated, and the optimal solution on the route is selected. By analyzing the feature vector of each data comprehensively and measuring the similarity of related topics, the links with high similarity are selected as the factors influencing the correlation degree of the objects to be retrieved. Experiment on big data collection of cross-border e-commerce is through Python platform. This paper tests the application of Python architecture in cross-border e-commerce, and provides technical support for the subsequent use of Internet crawlers to collect relevant information of cross-border e-commerce websites. Finally, a distributed crawler system architecture based on Python-Redis is proposed. The system includes Schedule and Item Pipeline modules based on Python platform. It is used for the secondary development of cross-border e-commerce projects. The system uses one master computer and four Slave computers. The results show that a distributed crawler can double the collection speed compared to a single crawler.