Session-based recommendation systems aim to predict user clicks using anonymous session data. Current models struggle with understanding complex item transition patterns and are affected by noise in the data, thus reducing accuracy. To address these problems, we introduce a self-supervised dual graph and intention association technique for session-based recommendations, named SDGIA (Self-supervised Dual Graph and Intention Association). SDGIA constructs a global undirected graph and session-directed graphs, enhancing information representation to capture transition patterns within and across sessions. A self-supervised learning mechanism improves feature extraction and generalization, meanwhile an intention association module filters out noise for more precise item representations. Experiments on three datasets demonstrate that SDGIA significantly outperforms existing models.