Session-based recommendation (SBR) predicts the next user interaction by exploiting the anonymous user’s short-term dynamic behaviour. The information available for SBR is limited, and some methods propose to extract the topological information of a session using graph neural networks. The existing problem is that graphs suffer from data sparsity. To this end, we propose Attribute-Enhanced Hypergraph Neural Networks for Session-based Recommendation (A-HGNN). First, the session is modelled as a hypergraph to enhance the integrity of the graph structure from a global view. Then, a hypergraph convolutional network is used for dual information aggregation to obtain item feature representations. On the other hand, item attributes from the local view are fused to accurately capture user preferences. Finally, contrastive learning is used to train the model to supervise and refine the learned session representations from both view. Experiments on two real-world datasets show that the A-HGNN recommendation outperforms previous superior methods.