Deep mining of e-commerce consumer behaviour data based on concept hierarchy tree

In order to solve the problems of low data collection efficiency, high noise, and low accuracy in traditional e-commerce consumer behaviour user data mining methods, a deep mining method for e-commerce consumer behaviour data based on concept hierarchy tree is proposed. Use Python scripting language to collect e-commerce consumer behaviour data from e-commerce platforms, and use Myriad filtering algorithm to remove the interference noise in e-commerce consumer behaviour data. Based on non-interference noise free e-commerce consumer behaviour data, utilising domain expert participation and machine learning algorithms, a concept hierarchical tree based e-commerce consumer behaviour data mining model is established to achieve deep mining of e-commerce consumer behaviour data. Experimental results show that the method proposed in this paper collects e-commerce consumer behaviour data more quickly, effectively removes interference noise contained in e-commerce consumer behaviour data, and can effectively and deeply mine the behavioural preferences of e-commerce consumers, with significant applicability. © 2024 Inderscience Enterprises Ltd.