To address the challenge of data sparsity within session recommendation systems, this study introduces a self-supervised graph convolution session recommendation model based on the attention mechanism (ATSGCN). The model constructs the session sequence into three distinct views: the hypergraph view, item view, and session view, showing the high-order and low-order connection relationships of the session. Secondly, the hypergraph view employs hypergraph convolutional networks to capture higher-order pairwise relationships among items within a conversation. The item view and session view employ graph convolutional networks and attention mechanisms respectively to capture lower-order connection details within local conversation data at both item and session levels. Finally, self-supervised learning is adopted to maximize the mutual information between the session representations learned by the two encoders, thereby effectively improving recommendation performance. Comparative experiment on the Nowplaying and Diginetica public datasets demonstrates the superior performance of the proposed model over the baseline model.