In the era of big data, e-commerce platforms have accumulated a large number of user behavior data, such as browsing, clicking, placing orders and adding commodities to shopping carts. How to use machine learning algorithms to explore the consumer preferences and habits behind big data has become a new research hotspot. This study mainly improves the user purchase prediction from two aspects: feature engineering and model building. After the deep understanding of e-commerce knowledge, we have constructed 115 features with statistical knowledge and data from many aspects such as users, commodities and comments. Moreover, a two-layer fusion model is designed. The first layer uses XGBoost, CatBoost, and logistic regression as the base classifiers which predict user purchase behaviors from different perspectives. The second layer employs a weighted average method to fuse the prediction results of the base class model, and its weight is generated by linear classifier learning. The experimental results show that the F1 score of the fusion model is higher than that of the individual classifier, and many times of experiments prove that the fusion model has high stability compared with the individual classifier.