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Software fault localization aim to improve localization accuracy by mining program and test cases execution data. Addressing the issue that SBFL technology relies too much on binary coverage information, a software fault localization method based on graph-interpretable networks is proposed. This method transforms test execution into a graph structure and utilizes graph attention networks to model and deeply mine the implicit information and interrelationships among code segments. It also applies reinforcement learning principles to explain the decision-making process after graph attention network learning, thereby determining key nodes and narrowing the fault localization range. The experiments were conducted on five projects from the Defects4j dataset and compared with SBFL and uninterpreted deep learning methods. The results showed that the localization method based on graph-interpretable networks improved the Top-1, Top-3, and Top-5 metrics by 7.26%, 7.56%, and 9.96%, respectively, and the EXAM index also increased by 8.98%, significantly outperforming other methods.