Online purchase prediction aims to predict users’ purchase behaviors， which can generate considerable commercial value for shopping websites. To address the problem of traditional models’ inability to learn implicit interest preferences from users’ historical behaviors accurately， a two-stage dynamic interest recognition model for online purchase prediction was proposed to predict the probability of users purchasing products. Firstly， at the first stage of the model， a click frequency graph of user-product pairs was constructed， and Light-Graph Convolutional Network （LightGCN） was utilized to learn contextual features of the graph as the static interest’s representation of users. Then， at the second stage， Bidirectional Gated Recurrent Unit （Bi-GRU） with attention mechanism was applied to explore the transformation process of user preferences. Finally， aiming at the potential high-dimensional features， a purchase prediction model integrating dynamic interest and implicit features was built. The extensive experimental results on two real e-commerce datasets show that compared with Graph Convolutional Network （GCN） model， the proposed model has the accuracy improved by at least 0.3 percentage points， and the F1 score improved by at least 2.05 percentage points.