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, DPC is heavily influenced by the truncation distance parameter. In order to address the issue of poor performance of DPC on datasets with uneven density distributions, a density peak clustering algorithm based on natural and weighted shared nearest neighbors is proposed. It first introduced natural nearest neighbor computations to calculate weights. Then, it redefined 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 were calculated. Finally, a novel strategy for distributing cluster centers was designed.