In order to tackle the problem that the original point cloud simplification was easy to lose key features and complex latent surface information, this passage proposed a weighted local optimal projection (WLOP) point cloud simplification algorithm based on FPFH. Firstly, this passage used Fast Point Feature Histogram (FPFH) to find and extract feature points in the original model. Then, the original dense point cloud was reduced by the WLOP algorithm to generate point cloud which had no noise, no outliers, and was evenly distributed. Finally, a point cloud fusion method was used to combine the feature points with the simplified model and remove redundant points. This passage carried out comparative experiments between algorithm with minimum rectangular bounding box algorithm, farthest point sampling algorithm and weighted local optimal projection. The experimental conclusion indicates that the algorithm in this paper is better than other algorithms in terms of distribution uniformity and feature retention when the reduction rate is 30%. In addition, the visual analysis results show that the algorithm in this paper not only guarantee the integrity of the simplified model, but also better preserve the key features of the original point cloud. The results of information entropy analysis show that the simplified point cloud contains richer information and expresses more accurate feature. The algorithm can provide important application value for point cloud reconstruction.