Clustering is an important research domain in data mining. For some knotty problems in clustering complex datasets, such as uneven densities among clusters, miscellaneous patterns of clusters and the identification of the centers, a clustering method is proposed based on relative density and decision graph, which introduces the idea of k-nearest neighbors to compute the relative densities of data points, and uses the clustering by fast search and find of density peaks(CFSFDP) algorithm for identifying central points, which can identify central points quickly and accurately and cluster datasets of arbitrary distribution effectively. The experimental results on seven typical test datasets show that the proposed clustering algorithm has good feasibility and performance. Compared with the classical density-based spatial clustering of application with noise(DBSCAN) algorithm and CFSFDP algorithm, the proposed algorithm has better clustering effect and accuracy, and has a wider range of adaptation.