Effective issue management is critical for the success of open-source projects on GitHub. However, the platform currently lacks the capability to identify implicit links between issues, complicating the management process. In this study, we propose a machine learning-based approach to identify and classify these links. Preliminary experimental results demonstrate that our approach significantly improves the detection of issue links, thereby aiding project management by structuring relationships and prioritizing tasks. These findings suggest that the proposed approach has the potential to enhance issue tracking in large-scale GitHub projects.