Combined with the local features of point cloud and Octree optimization search,a n automatic registration algorithm of 3D deformation point cloud for machining pro cess 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 th e invalid points and noise points in the main body.The normal vector and three feature elements of t he point cloud are calculated as the input of the point pair feature net (PPFNET) feature learning method.The deformed loca l 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 r elationship between disordered point clouds and complete the rough registration process of point clo uds.Then,an improved precision registration algorithm based on interative closest point (ICP) is proposed.By increasin g the threshold limit and filtering the influence of chatter during machining deformation,the registr ation 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 co mparison between the analysis results and the experimental results shows that the mean absolut e percentage error (MAPE) is 2.32%.The simulation results show that the proposed m ethod meets the requirements of real-time and measurement accuracy of machining deformation.