In order to solve the problem that the clustering centers cannot be determined automatically by Density Peaks Clustering (DPC) algorithm, and the clustering center points and the non-clustering center points are not obvious enough in the decision graph, Comparative density Peaks Clustering algorithm with Automatic determination of clustering center (ACPC) was designed. Firstly, the distance parameter was replaced by the distance comparison quantity, so that the potential clustering centers were more obvious in the decision graph. Then, the 2D interval estimation method was used to perform the automatic selection of clustering centers, so as to realize the automation of clustering process. Experimental results show that the ACPC algorithm has better clustering effect on four synthetic datasets; and the comparison of the Accuracy indicator on real datasets shows that on the dataset Iris, the clustering accuracy of ACPC can reach 94%, which is 27.3% higher than that of the traditional DPC algorithm, and the problem of selecting clustering centers interactively is solved by ACPC.