Considering the lack of extraction and utilization of higher-order features and data sparsity in current graph neural network-based session recommendation methods, a self-supervised session recommendation incorporating dynamic multi-level gated graph neural network (GGNN) and hypergraph convolution (SDMHC-GNN) is proposed. Firstly, different graph structures are used to model the session sequence into three different views: session view, hypergraph view, and relational view. The session view uses dynamic multi-level gated graph neural networks, sparse self-attention, and sparse global attention mechanisms to generate local sequential session representations. The hypergraph view uses hypergraph convolution and soft attention mechanisms to generate higher-order session representations. The relational view uses graph convolution and sparse cross-attention mechanisms to generate session relational representations. Secondly, the mutual features among different session representations are maximized by self-supervised learning. Finally, the current session representation is filtered and enhanced by the intentional neighbor collaboration module. Multiple experiments are conducted on two public data sets, Diginetica and Tmall, and compared with advanced baseline models. The experimental results indicate that the performance of the proposed model is superior to that of the baseline model, proving the effectiveness of the model.