Combined with the local features of point cloud and Octree optimization search,an automatic registration algorithm of 3D deformation point cloud for machining process measurement of thin-walled parts is proposed,and the displacement deviation is effectively calculated.Firstly,the data of the point cloud model of thin-walled parts is preprocessed to remove the invalid points and noise points in the main body.The normal vector and three feature elements of the point cloud are calculated as the input of the point pair feature net (PPFNET) feature learning method.The deformed local features are aggregated into the global features by using the maximum pool layer.Through the in-depth learning of the global and local feature descriptors,it can find out the corresponding relationship between disordered point clouds and complete the rough registration process of point clouds.Then,an improved precision registration algorithm based on interative closest point (ICP) is proposed.By increasing the threshold limit and filtering the influence of chatter during machining deformation,the registration accuracy is 98.58% and the registration efficiency is improved by 10%.Finally,Hausdorff is used to calculate the distance,and Cloud-Compare is used to analyze the displacement deviation.The comparison between the analysis results and the experimental results shows that the mean absolute percentage error (MAPE) is 2.32%.The simulation results show that the proposed method meets the requirements of real-time and measurement accuracy of machining deformation.