Abstract: The conventional optimization methods for agricultural e-commerce user classification mainly use the method of memory load classification on user data to obtain classification results, ignoring the impact of data dimensions on classification results, resulting in low similarity of user classification results. Therefore, a decision tree algorithm based optimization method for agricultural e-commerce user classification is proposed. Cluster agricultural e-commerce user data using spatial mapping method to obtain low dimensional coordinates of the clustering data, obtain user data with lower dimensions, extract classification feature values from the low dimensional data, and obtain classification variance based on decision tree algorithm to achieve user classification. The experimental results show that the average similarity of the classification results obtained after the application of the proposed method is 0.96, and the classification results are relatively accurate with good optimization effects, meeting the practical needs of agricultural e-commerce operations.
王扬宇, 潘园园. 基于决策树算法的农业电商用户分类优化方法研究[J]. 吉林化工学院学报, 2025, 42(9): 80-85.
WANG-Yangyu, PAN-Yuanyuan. Research on Optimization Method for Agricultural E-commerce User Classification Based on Decision Tree Algorithm. Journal of Jilin Institute of Chemical Technology, 2025, 42(9): 80-85.
