Concerning the problem that the cutoff distance needs to be selected manually in Clustering by Fast Search and Find of Density Peaks （CFSFDP） algorithm， as well as the poor clustering effect on complex datasets with different density clusters due to the error caused by nearest neighbor assignment， a Density Peak Clustering algorithm based on Adaptive Reachable Distance （ARD-DPC） was proposed. In this algorithm， a non-parametric kernel density estimation method was used to calculate the local density of points， and the clustering centers were selected by the decision graph. Then， an adaptive reachable distance was used to assign the data points and obtain the final clustering result. Simulation experiments were conducted on 4 synthetic datasets and 6 UCI datasets， and the proposed algorithm was compared with CFSFDP （Clustering by Fast Search and Find of Density Peaks）， DBSCAN （Density-Based Spatial Clustering of Applications with Noise） and DADPC （Density Peaks Clustering based on Density Adaptive distance）. Experimental results show that compared to the three other algorithms， the proposed ARD-DPC algorithm achieves the all highest Normalized Mutual Information （NMI）， Rand Index （RI） and F1-measure on 4 synthetic datasets and 3 UCI datasets， the only highest NMI on UCI Breast dataset， the only highest F1-measure on UCI Heart dataset， but does not cluster UCI Pima dataset well， which has high fuzzyness and unclear clustering feature. At the same time， ARD-DPC algorithm can accurately identify the number of clusters and clusters with complex density on the synthetic datasets.