The invention discloses a three-dimensional point cloud model classification method based on convolution neural network, includes selecting Princeton ModelNet to generate training set and data set from training data and test data by selecting required number of models from official website according to ModelNet 10 and ModelNet 40 respectively, selecting training data and test data from official website according to Princeton ModelNet, selecting Princeton ModelNet to generate training set and data set according to model Net 10 and ModelNet 40 respectively, and selecting Princeton ModelNet to generate training data and test data. 2, carry out feature analysis on that point cloud model and constructing a classification framework; S3, ordering the point cloud; S4, two-dimensional visualizing the ordered point cloud data; S5, Constructing CNN network for two-dimensional point cloud image. The invention applies the CNN in the image field directly to the classification of the three-dimensional point cloud model for the first time, 93.97% and 89.75% classification accuracy were obtained on ModelNet 10 and ModelNet 40 respectively, Experimental results show that it is feasible to classify 3D point cloud model by using CNN in image domain. PCI2CNN proposed in this paper can capture 3D feature information of point cloud model effectively and is suitable for classification of 3D point cloud model.