For the problems of losing the model feature information and causing easily the point cloud surface holes in single simplification algorithms, a streamlined algorithm for curvature classification optimization based on dichotomous k-means clustering is proposed. First, the least squares method was used to fit the neighborhood surface, calculate the curvature value, and divide the significant and non-significant feature regions based on the curvature value，Second, dichotic k-means clustering was used to divide non-significant feature regions, select the subfeature points with feature importance retained according to the curvature threshold of subclusters, and finally the datasets and subfeature points were merged to obtain simplified results. The simplification algorithm is compared with the space surrounding box algorithm and the curvature reduction algorithm by the simulation experiments in terms of speed and information entropy. The results show that the proposed algorithm outperforms the other two algorithms in streamlining quality and has a certain application value in point cloud data reconstruction.