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Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user\u2019s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors was extracted from three perspectives\u2014user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. 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