Density peaks clustering has poor clustering effect on data with different densities, and the problem of the ripple may occur in the sample allocation process. To solve the problems, a new clustering method, density peaks clustering based on mutual neighbor degree, is proposed. Firstly, the method introduces k nearest neighbors to calculate local density, so as to ensure the relativity of the density. Then, it defines the metrics of mutual neighbour degree with global and local features of data. We propose a new allocation strategy according to the metrics. The strategy adopts k nearest neighbors to find the density peaks, assigns the k nearest neighbors to their corresponding clusters, searches the unallocated data points with the highest mutual neighbor degree for all allocated data points, and then assigns the unallocated points to the cluster where the allocated data points are located. The experiments on synthetic and UCI datasets show that the proposed method outperforms DPC, DBSCAN, OPTICS, AP, K-Means and other DPC variant algorithms.