To move the outliers and noisy points away from 3D point cloud data and to maintain the sharp features of the model simultaneously, a feature-based weighted fuzzy C-means point cloud denoising algorithm was proposed. Firstly, the point cloud was organized by K-D tree data structure and the large-scale outliers were removed by the statistics of r radius neighboring points. Then, the principal component analysis method was adopted to estimate the curvature and normal vector of point cloud data and the patches with distinguished features were identified according to the curvature feature weight. Pursuant to different feature regions, the feature-preserving weighted fuzzy C-means clustering algorithm was adopted to denoise for the patch with rich feature information and the fuzzy C-means clustering algorithm was adapted to denoise for the patch with less feature information, respectively. Finally, a bilateral filter was used to smooth the data set. The algorithm was verified and the experimental results show that the max denoising error is limited to 0.15% of the model size and the min denoising error is limited to 0.03% of the model size. In conclusion, this approach moves efficiently and precisely the noise with different scales and intensities in point cloud, meanwhile performing a feature-preserving nature. Moreover, it is robust enough to different noise models.