ing at the problems of the co-analysis of multiple 3D models in the function space and the co-segmentation of the whole model cluster, we propose a co-segmentation method based on point cloud sparse coding. First, the point cloud feature is extracted and the 3D information is transformed into the feature space. Second, the dictionary matrix and sparse vectors are constructed after the decomposition of the feature vectors into the base vectors by the deep learning network. Finally, the test data is represented by dictionary sparseness and the category of each point in the point cloud model is determined. To get the co-segmentation result, the homogeneous points are divided into the same region. The experimental results show that the segmentation accuracy on ShapeNet Parts dataset obtained using the proposed algorithm is 85.7%. Compared to the current mainstream algorithms used for segmentation, the proposed algorithm can not only compute the relational structure of model clusters more effectively, but also improve the segmentation accuracy and effect.