A Multi-channel graph attention network social recommendation model with disentangling capability was proposed. This model mainly included three modules:the deep clustering module, the aggregation module based on multi-channel graph attention network, and the rating prediction module. Among them, the deep clustering module was used to group users and items. The clustering results can be used to split user-user social graph and user-item interaction graph into multiple subgraph to learn user interest groups and users′ interests in different types of items. The aggregation module learns the attention of different sub-graphs to the prediction results. The rating prediction module input the learned user representation vector and item representation vector into the multilayer perceptron for rating prediction. Extensive experiments on multiple real-world datasets demonstrate that the proposed method is better than other social recommendation algorithms. Specifically, compared with the latest graph neural networks method for social recommendation, the root mean square error is respectively reduced by 2.26% and 2.07% on the Ciao and Epinions datasets, and the mean absolute error is respectively reduced by 2.58% and 3.06%.