In a recommendation system, users’ interest in items changes dynamically and is affected by various factors such as users' own historical behaviors, their friends’ historical behaviors, and even short-term hot spots. How to describe users’ temporal interests in a recommendation system and extract effective information has always been one of the challenges for the recommendation algorithms. On the basis of the Graph Neural Network (GNN) recommendation algorithm, we propose an improved graph network algorithm based on the Attention Gated Recurrent Unit (Attention-GRU) in this study. Furthermore, feature modeling is performed on the temporal interactive history of users and items, and in combination with social network, the temporal characteristics are transmitted between users and items. In addition, the proposed algorithm is verified on the Ciao and Epionions data sets and compared with other related work, proving that the model proposed in this study can effectively extract the temporal characteristics of users and items and improve the effectiveness of the recommendation systems.