Bug localization is a critical aspect of software maintenance, and improving the effectiveness and efficiency of automated fault localization has become a central research focus in software engineering. With the surge in open-source software and the increasing demand for software hot updates, automated bug localization focused on change sets has become a key tool for software quality assurance. Traditional bug localization methods based on information retrieval can only represent textual information and fail to fully account for structural and semantic changes within change sets, making them unsuitable for direct application in change set bug localization tasks. Therefore, this study proposes a method for change set bug localization based on graph Transformer, which uses an abstract syntax tree to represent change information and capture code structure changes. The method represents both local and global semantic information of the changed code and bug reports, enabling the matching and localization of bug information within change sets. To validate the effectiveness of the proposed method, it is tested on bug reports and changes from six sets of bug-inducing change sets. Compared to the state-of-the-art models, the proposed method demonstrates improvements of 11.4% and 12.9% in MAP and MRR metrics, respectively, validating the efficacy of the proposed approach.