This paper reviews the importance, challenges, and development prospects of graph clustering technology in the field of data analysis. As an effective data analysis tool, graph clustering shows great potential in revealing the internal structure and patterns of data, and is widely used in fields such as social network analysis, bioinformatics, and recommendation systems. This paper introduces the basic concepts and methods of graph clustering, including traditional spectral clustering, modularity optimization, and graph neural network clustering methods based on deep learning. At the same time, it analyzes the challenges faced by graph clustering in practical applications, such as the increase in data scale and complexity, the bottleneck of computational efficiency, and the stability of clustering quality. Finally, the development direction of graph clustering technology is prospected, including algorithm innovation, interdisciplinary integration, and practical application expansion, providing strong support for solving complex data analysis problems. This paper provides valuable references and inspirations for scholars and practitioners in the field of graph clustering, and promotes the continuous development and application of graph clustering technology.