The density clustering algorithm has been widely used because of its robustness to noise and the ability to find clusters of any shapes. However， in practical applications， this type of algorithms faces the problem of poor clustering effect due to the uneven distribution of the densities of different clusters in the dataset and the difficulty of distinguishing the borders between clusters. In order to solve the above problem， a Varied Density Clustering algorithm based on Border point Detection （VDCBD） was proposed. Firstly， the border points between varied density clusters were recognized based on the given relative density measurement method to enhance the separability of adjacent clusters. Secondly， the points in the non-border area were clustered to find the core class structures of the dataset. Secondly， the detected border points were allocated to the corresponding core class structures according to the principle of high-density neighbor allocation. Finally， the noise points in the dataset were recognized based on the class structure information. The proposed algorithm was compared and analyzed with the clustering algorithms such as K-means， Density-Based Spatial Clustering of Applications with Noise （DBSCAN）algorithm， Density Peaks Clustering Algorithm （DPCA）， CLUstering based on Backbone （CLUB）algorithm， Border Peeling clustering （BP）algorithm on artificial datasets and UCI datasets. Experimental results show that the proposed algorithm can effectively solve the problems of uneven distribution of density and indistinguishable borders， and is superior to the existing algorithms on the evaluation indicators of Adjusted Rand Index （ARI）， Normalized Mutual Information （NMI）， F-Measure （FM）， and Accuracy （ACC）； in the analysis of operating efficiency， when the data size is relatively large， the operating efficiency of VDCBD is higher than those of DPCA， CLUB and BP algorithms.