In the process of point cloud denoising, after removing large-scale noise points from the point cloud data, there are usually small noise points mixed around the point cloud that are difficult to directly remove. This seriously affects the smoothness of the reconstructed surface and leads to a certain degree of feature distortion in the model. Thus, for small-scale noise points, this study proposes a point-cloud-guided filtering algorithm based on optimal neighborhood feature weighting. The optimal initial neighborhood is selected based on the information entropy function, and feature points are identified by combining surface and normal variations with distance features. The neighborhoods of the feature points are adaptively grown to obtain a smooth neighborhood. The guided filtering algorithm is adjusted by surface variation weighting to achieve anisotropic smoothness of the feature and non-feature parts of the complex surface part. As evidenced by experimental results, the proposed algorithm exhibits a more obvious smoothing effect on noisy point clouds, performs better in feature retention, and is significantly more efficient than several commonly used smoothing algorithms.