Neighbor propagation based spectral clustering can be used to cluster the dataset with inhomogeneous density. However, sometimes it propagates different clustering samples into the same subset with high similarity, which can not obtain the real similarity matrix and accurate clustering results. To solve this problem, a local density estimation and neighbor propagation based spectral clustering algorithm (LDENP-SC) is proposed. In this algorithm, the local density of the samples is firstly estimated and the dimensions of the datasets are increased. Then, the similarity matrix is updated by using neighbor propagation and the new dataset is clustered by spectral clustering. Also, a simple local density estimation method is proposed by with the local density of the samples can be estimated accurately and fast. Moreover, based on propagation algorithm, a method for updating the similarity of the samples in different subsets is adopted to get more actual similarity matrix. The experimental results show that LDENP-SC algorithm can obtain similarity matrix close to the ideal and accurate clustering results, has good generalization ability and is robust to a certain range ofparameter σ.