Abstract: Considering the difference of local statistical properties between data points,and combining with K-means algorithm,a novel clustering algorithm based on statistical manifold was proposed. By calculating the mean and covariance of the neighborhood of the data points,the original data point cloud was mapped to the normal distribution family manifold to form the parameter point cloud. Different measurement structures were constructed on the normal distribution family manifold,and K-means method was applied to cluster the parameter point cloud,so as to classify the corresponding original data. To verify the effect in the point cloud denoising,the algorithms based on different difference function were used to denoise the point cloud with high density noise,and simulation analysis was carried out. The simulation results show that the algorithm using KL divergence as difference function can get a better denoising effect,verifying the potential of the algorithm in denoising application.