The collected point cloud data contains a lot of redundant data,which brings a lot of inconvenience to the later data processing and its application.However,the point cloud compressed by the existing general compression method is likely to cause the loss of detailed features.Therefore,this paper proposes a point cloud data compression method based on the extraction of feature points and SIFTS key points.The core technology of this method is to first extract the boundary points according to the angle between the query point and the vector formed by the points in the neighborhood.Then extract the sharp points according to the curvature of the point cloud data and the angle between the normal vectors,so that the feature points are absolutely retained during the cloud compression process.At the same time,SIFT key points are extracted in flat areas,which can avoid the retention of feature points in areas where the curvature changes slowly.Finally,the feature points and SIFT key points are merged to realize the compression processing of the point cloud data.The study is compared with the existing two methods based on curvature compression,and the results show that the method proposed in this paper can not only remove redundant data to the greatest extent,but also retain most of the feature points in the point cloud.High quality compression of point cloud data is achieved.