Abstract
Aiming at the problem of large mean square error of regression coefficient in traditional e-commerce platform consumer purchase behavior preference analysis, this paper designs an e-commerce platform consumer purchase behavior preference analysis based on lightgbm algorithm. Firstly, the index system of consumer purchase behavior preference on e-commerce platform is constructed. Then, based on lightgbm algorithm, the classification variables of consumer purchase behavior preference data are trained. Then, the weight of consumer purchase behavior preference index is calculated. Finally, the analysis curve of consumer purchase behavior preference on e-commerce platform is fitted to realize preference analysis. The experimental results show that the mean square error of regression coefficient of the experimental group is significantly lower than that of the control group, which can solve the problem of large mean square error of regression coefficient of traditional preference analysis.
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