Research on Cross-border E-commerce Consumer Behavior Based on Spatio-temporal Data Mining Calculation in the Perspective of Digital Economy Globalization

Thanks to the wave of digital economic globalization, the business development of cross-border e-commerce platforms is in full swing.This paper aims to promote the development of e-commerce personalization and launch the research of consumer behavior characteristics.This paper utilizes the concept of entropy in information theory to modify the weights of user feature vectors, so as to make up for the inadequacy of the K-Means algorithm in expressing ambiguous clustering information.Combined with the data samples, the consumer behavior prediction model is established.For the dynamic clustering of customer groups, construct the customer segmentation model based on the improved K-Means algorithm.Combined with the time series prediction model, complete the formation of the spatio-temporal data mining model of consumer behavior.The model is used to mine the consumer behavior dataset of a cross-border e-commerce platform, and the clustering analysis yields four precise consumer group portraits.In this paper, by mining and analyzing the characteristics of consumer spatio-temporal data, the cross-border e-commerce platform is provided with more accurate user insights and marketing optimization solutions.