Density Peak Clustering(DPC) divides the data according to the density and distance attributes of points, which can achieve better clustering results for most data sets. However, the nearest neighbor allocation method of DPC will cause large errors for the data sets with overlapping. Aiming at this defect, a multi neighbor voting clustering method is proposed, which uses the voting results of multiple neighbors to determine the ownership of unknown points. Numerical experiments show that the density peak clustering algorithm based on voting method outperforms general DPC when facing overlapping data sets.