To improve the registration accuracy of a point cloud, the problem of poor robustness of the iterative closest point (ICP) algorithm under the condition of noise interference and data loss caused by a single feature needs to be solved. Accordingly, a point cloud registration method based on weighting neighborhood surface deformation information is proposed. First, to simplify the neighborhood information of points, a neighborhood construction method based on the number of neighboring points as the constraint is proposed, and considering the influence of neighbors on the sampling points, a weighting method is introduced to improve the extraction efficiency of the intrinsic shape signature (ISS) feature point extraction algorithm. Second, the mean value of the normal vector inner product of the neighborhood is calculated to perform the second feature point extraction of the point cloud. Then, the fast point feature histogram (FPFH) is used to describe the feature, and the double constraint condition is used to determine the matching point pair relationship. Finally, in the registration phase, accurate registration is achieved by using the bidirectional k-tree ICP (DTICP) algorithm. Experiment results reveal that the proposed algorithm can effectively register missing point clouds in a noisy environment with better robustness and anti-interference compared with the classical ICP algorithm.