To address the issues faced by traditional Iterative Closest Point （ICP） algorithms in handling complex point cloud spatial features， such as noise interference and data loss leading to slow convergence， low registration accuracy， and pool robustness， this paper proposed a point cloud matching algorithm based on adaptive local neighborhood conditions. Initially， voxel grid filtering was used for data preprocessing， and the curvature of neighborhood surfaces was defined based on the distribution of nearby points within different radii. Considering the distribution of normal vectors and neighborhood curvature features， more accurate feature points were extracted. Subsequently， the most significantly changing curvature feature points in the neighborhood were further extracted using the least squares surface fitting method. These points were described using the Fast Point Feature Histograms （FPFH）， and similar feature point pairs were matched using a sample consensus algorithm with a set distance threshold. This calculated the key coordinate transformation parameters to complete the initial registration. Finally， a linear least squares optimization point-to-plane ICP algorithm was used to achieve more accurate registration results. Comparative experiments demonstrate that， under conditions of noise interference and data loss， the proposed method improves registration accuracy by an average of 45% and increases registration speed by 38%， compared to existing algorithms （ICP， SAC-IA+ICPK4PCS+lCP）， thus confirming its excellent robustness in handling large-volume， low-overlap point cloud registrations.