Point cloud registration technology is the core technology of point cloud data processing. The quality of the point cloud will influence the registration effect of point cloud registration. An excellent quality point cloud can improve registration accuracy， spatial integrity， and slam performance. Therefore， assessing the quality of point cloud data has significant objective value. The point cloud data obtained by the sensor contains noise such as systematic and nonsystematic errors. In this case， point cloud data processing becomes crucial. However， there is no more objective method to evaluate the treatment effect. A quantitative evaluation method of multidimensional point cloud structure similarity is proposed. This method compares the point cloud data before and after filtering with the standard data. The mean， standard deviation， and covariance of all point coordinates on the three-dimensional coordinate axis are compared. Subsequently， the structural similarity values on the three coordinate axes are weighted. Finally， the similarity and correlation degree of the three-dimensional structure is obtained. Then， it realizes the evaluation of point cloud filtering， point cloud sparse， and point cloud data quality. The method is also verified by experiments to improve registration accuracy. Experiments demonstrate its capacity to evaluate the quality of the 3D point cloud. It can evaluate the quality of the point cloud obtained under different noise types and processing methods. It provides a reference for point cloud registration. This method improves both the accuracy and efficiency of cloud point registration， as well as its quality.