Performance optimization in software engineering is crucial for enhancing user satisfaction and maintaining competitive advantage. Traditional methods for detecting performance issues – dynamic profiling and static analysis – often fall short in addressing complex dependencies within software architectures. This paper introduces the HyperGraph Neural Network (HGNN), a novel approach that leverages both static and dynamic program analysis to identify and prioritize performance bottlenecks effectively. By analyzing inter- connected method call within fundamental patterns, HGNN and its enhanced version, HGNN+, utilize hypergraph neural network techniques to capture and learn dependency relationship features, significantly improving detection accuracy. Our initial testing on the PDFBox project demonstrated that HGNN+, especially in com- bination with CodeGPT and GraphCodeBERT present promising results compared to traditional methods. These findings underline HGNN+’s ability to manage complex dependencies and offer a scal- able solution for software performance engineering. The benefits observed across multiple initial tests promise a broad applicability for the approach, setting a solid foundation for future research and expansion to more diverse datasets and neural network models, enhancing the reliability and effectiveness of performance optimiza- tion detection.