In order to further improve the recognition and classification accuracy of large-scale multi-category point cloud model, a K-nearest neighbor convolutional neural network is proposed. First, the point cloud model is uniformly sampled with the farthest point sampling algorithm. Second, the K-nearest neighbor algorithm is used to construct the local neighborhood of each point for the sampled point cloud model. In order to prevent the non-local diffusion of information, a local neighborhood is constructed for each feature extracted from the convolution layer. Then, all local features are aggregated to obtain the global feature representation of the point cloud model through the max pooling. Finally, the probabilities corresponding to each category are calculated and classified using the fully connected layer and Softmax function. Experimental results show that the recognition accuracy of this algorithm on the ModelNet40 dataset is 92%. Compared with the current point cloud model recognition and classification algorithms, the proposed algorithm can more effectively fuse local structure features and improve the accuracy of point cloud model recognition and classification.