Graph neural networks (GNN) have been applied in the session-based recommendation, which aims to predict the potential items the user will interact with next time based on the given anonymous interaction sequences. Most advanced GNN-based recommendation methods (e.g., LESSR, SGNN-HN and GCE-GNN) focus on how to either capture long-range dependencies or use global context information to enhance the recommendation performance. However, these approaches do not consider whether they can enhance each other and cooperatively improve performance. In addition, these methods mainly take the representation of the last interacted item as current interest, ignoring the potential information in other recent interactions and neighbor sessions. To solve these issues, we propose a model called LDGC-SR to integrate Long-range Dependencies and Global Context information for Session-based Recommendation. LDGC-SR employs normalization and adaptive weight fusion mechanism (NAWFM) to integrate long-range node information and global context information to alleviate the over-smooth problem of item representation and the over-fitting problem of the model. NAWFM can reduce the numerical bias between long-range dependencies and global context information, and dynamically adjust their importance to the user’s preference. Moreover, LDGC-SR employs a global context enhanced short-term memory module (GCE-SMM) to capture the user’s current interest more accurately and incorporate it as an auxiliary task. The experimental results demonstrate that our proposed method performs better than the state-of-the-art methods on all four public datasets, and provide suggestions for future session-based recommendation systems.