Big Data-Based E-Commerce Transaction Information Collection Method

Abstract

With the rapid development of e-commerce industry, online shopping has become a craze. With the rapid growth of transaction volume on e-commerce platforms, a large amount of transaction data has been accumulated. From the transaction information of these users, a lot of very valuable information can be mined, such as the defects of products and the actual needs of users. In view of the existing e-commerce transaction information collection method is not mature, in this paper, the electric business platform system architecture planning and design increases the function management module. In this paper, a new Naive Bayes model is established by using HBase distributed database instead of traditional database. Based on the optimization and extraction of the important transaction information in the product, the dataset of e-commerce transaction information is updated. Through the efficiency test of the collection method, the information scalability ability test, and the accuracy test, the important context was sorted out after integration, the sources of trading information were sorted out, and the data analysis of the collected information was conducted to optimize the information collection method and verify the feasibility of the method.