Abstract: To overcome the drawbacks of traditional session-based recommendation system （SBRS） models， which often ignore the interaction between item clicks and omit the relative position between items within a session， this study investigated a Hawkes process and graph neural network （HPGNN） method for session-based recommendation. This novel method incorporated a dual-stream structure consisting of a graph neural position-aware layer and graph neural Hawkes layer to learn the long- and short-term user preferences， respectively. The graph neural position-aware layer captured the interaction between nodes by using a gated graph neural network （GGNN） to obtain the hidden vector representations of each item in a session. Furthermore， we introduced a gradually decreasing residual network that combined previous encoding information with the current network. In addition， we used a position-aware attention network to capture the position information of item nodes in the session to learn the long-term preferences of users. The graph neural Hawkes layer combined the GGNN and Hawkes process to represent the users’ short-term preferences more accurately by capturing the relationship between item click counts over time. Finally， we linearly combined the two layers to better describe users’ intent. Experimental results showed that our proposed HPGNN outperformed other existing state-of-the-art SBRS models on the Diginetica and Yoochoose1/64 datasets.