Session-based recommendation algorithms only capture users’ short-term dynamic interests, ignoring the impact of long-term interests and social friends on their behavior. To address the problem, a recommendation algorithm combining social influence and long short-term preferences was proposed. Firstly, a novel heterogeneous relation graph was designed to organize users’ social relations and historical interaction behaviors. And a heterogeneous graph neural network based on the attention mechanism was proposed to learn the graph, and to obtain long-term preference for integrating social influence of users. Moreover, considering the problem of noise caused by inconsistent social influence, a weighted and pruning strategy was proposed to reduce noise interference and enrich the graph structure information. Then, a lossless session modeling method was used to capture users’ short-term preference. Finally, users’ short-term preference and long-term preference were adaptively fused to obtain a feature representation that reflects users’ global preferences. Experimental results on Gowalla and Delicious datasets show that the indicators of the proposed method are significantly improved compared with the existing advanced methods, which proves the effectiveness of the proposed algorithm.