Challenges in point cloud registration arise from noise in captured scene data, often due to environmental lighting and equipment limitations, leading to incomplete scene capture and issues such as low accuracy, slow iteration, and susceptibility to local optima. To address these issues, we propose a point cloud template matching method that leverages an enhanced Fast Point Feature Histogram (FPFH) feature extraction technique. This method uses the characteristics of moving least squares (MLS), which can smooth the fluctuation data and repair the holes in the point cloud, so as to improve the feature weight of the FPFH descriptor and optimize the corresponding relationship between the source point cloud and the target point cloud. Finally, the corresponding relationship is used for the initial registration of sampling consistency (SAC-IA) and the iterative closest point (ICP) registration to obtain the final transformation matrix. This approach demonstrates a 35% reduction in iteration count and an 82% improvement in registration accuracy compared to traditional algorithms when matching the target point cloud within the scene data. The method exhibits high accuracy, robustness, and reliability in point cloud registration tasks.