Abstract: Aiming at the problems of insufficient extraction of users’ main interest preferences in session-based recommender algorithms based on graph neural networks, a Session-Based Recommender Method Based on Interest Attention Network (SR-IAN) is proposed. First, the graph neural network is used to obtain the context transformation relationships between the items, and the graph embedding vectors of the items are obtained; Secondly, the graph embedding vector input into the interest attention network to extract the user’s main interest preferences; Then the graph embedding vectors of the items are weighted by the attention layer; Finally, the click probability values of the candidate items are obtained through the prediction layer and sorted. The proposed algorithm model was verified by experiments on three public datasets Diginetica, Retailrocket and Tmall, which showed an improvement of 0.942%, 1.183% and 2.977% compared with the baseline model on MRR@20. Besides, the time complexity of the model is reduced, which verifies the effectiveness and high efficiency of the proposed method.