Abstract: Online purchase prediction aims to predict a users’ purchase behavior, which can generate considerable commercial value for shopping websites. Currently, most purchase prediction studies ignore the exploration of implicit preferences. Thus, this paper proposes a two-stage dynamic interest recognition model for online purchase prediction. First, the first stage of the model constructs a click frequency graph of user-item pairs and utilizes a light graph convolutional neural network to learn contextual characteristics as their static interest’s representation. Then, the second stage invokes Bi-GRU with attention mechanism to explore the transformation process of user preference. Finally, aiming at the potential high-dimensional features, a purchase prediction model integrating dynamic interest and implicit features is built. This paper conducts extensive experiments on two real e-commerce datasets. Moreover, the results validate that the proposed model can greatly improve the performance of purchase prediction from accuracy and F1 score.