Session-based recommendation aims to predict the next item a user will interact with based on a series of items. Most existing session-based recommender systems do not fully utilize the temporal interval information between items within a session, affecting the accuracy of recommendations. In recent years, graph neural networks have gained significant attention in session-based recommendation due to their strong ability to model complex relationships. However, session-based recommendations that rely solely on graph neural networks overlook the hidden high-order relationships between sessions, resulting in less rich information. In addition, data sparsity has always been a phenomenon in recommender systems, and contrastive learning is often employed to address this issue. However, most contrastive learning frameworks lack strong generalization capabilities due to their singular form. Based on this, a session-based recommendation model combined with self-supervised learning is proposed. First, the model utilizes the temporal interval information between items within user sessions to perform data augmentation, enriching the information within the sessions to improve recommendation accuracy. Second, a dual-view encoder is constructed, combining a hypergraph convolutional network encoder and a Transformer encoder to capture the hidden high-order relationships between sessions from multiple perspectives, thus enhancing the diversity of recommendations. Finally, the model integrates the augmented intra-session information, the multi-viewed inter-session information, and the original session information for contrastive learning to strengthen the model’s generalization ability. Comparisons with 11 existing classic models on 4 datasets show that the proposed model is feasible and efficient, with average improvements of 5.96% and 5.89% on HR and NDCG metrics, respectively.