Density Peak Clustering (DPC) has been widely used as an efficient and non-iterative clustering algorithm. However, studies have found that DPC struggles to select correct cluster centers, especially in datasets with non-spherical clusters and non-uniform density. Moreover, the algorithm is heavily influenced by the truncation distance parameter. 【Objective】In order to address the issue of poor performance of DPC on datasets with uneven density distributions, 【Method】we propose a density peak clustering algorithm based on natural and weighted shared nearest neighbors. This algorithm first introduces natural nearest neighbor computations to calculate weights, then redefines the similarity between data objects based on the definitions of first-order and second-order shared nearest neighbors. Subsequently, by fusing the definitions of shared nearest neighbor similarity and natural nearest neighbor weights, relative density and relative distance are calculated. Finally, a novel strategy for distributing cluster centers is designed. 【Result】Experimental results on sixs different types of datasets demonstrate that the proposed algorithm outperforms four other comparative algorithms significantly in terms of clustering performance. 【Conclusion】The method achieves better cluster center identification on datasets with non-uniform density, effectively addressing the aforementioned issues.