As a simple three-dimensional expression,point cloud has been widely applied in the three-dimensional digitalization of cities.However,the complexity and variability of urban objects lead to the fact that compared with other cloud sites;point cloud is more complex,with higher difficulty in denoising and higher requirement for denoising accuracy.In order to solve the problem of urban point cloud denoising,this paper designs a new algorithm based on high-dimensional feature density space,using least-squares density clustering constraint and following the standard threshold principle.This algorithm first constructs the high-dimensional characteristic density space,then solves the density fitting curve of each dimension with the least square algorithm,and finally extracts the intersection of the convergence point set of each dimension according to the standard threshold principle,that is,the target point set.The experiment shows that the algorithm in this paper has high precision and good removal effect for point cloud in urban scene,which meets the requirements of urban point cloud denoising task and achieves the expected effect.