In the surface geometric features measurement of automobile structural parts, the traditional point cloud simplification method will destroy the geometric features in the point cloud at a high simplification rate, reducing the integrity and dimensional accuracy of geometric features. To solve this problem, a point cloud simplification method for geometric feature preservation was proposed. Firstly, the K-means clustering of point clouds was carried out based on the idea of spatial region segmentation, and geometric feature descriptors were constructed to extract feature region point clouds by calculating information entropy in the cluster. Secondly, the iterative clustering reduction based on fuzzy C-means (FCM) & K-means was carried out for the point cloud of feature region, and octree reduction was carried out for the point cloud of non-feature region. Finally, the simplified point clouds of different regions are spliced to achieve the purpose of simplification. The results show that the proposed method can completely retain the geometric features of the model surface and avoid the appearance of holes. At the simplification rate of 94. 30%, the maximum error between the simplified point cloud and the original point cloud is 0. 912 mm, and the root mean square error is 0. 041 mm, which is more accurate than the traditional method.