1. A deep learning based method for three dimensional (3D) model triangular facet feature learning and classifying, comprising: constructing a deep convolutional neural network (CNN) feature learning model having a first convolution layer, a first downsampling layer, a second convolution layer and a second downsampling layer, wherein the first convolution layer has 16 convolution kernels, each of which has a dimension of 5×5, a scaling factor of the first downsampling layer is 2, the second convolution layer has 16×20 convolution kernels, each of which has a dimension of 3×3, and a scaling factor of the second downsampling layer is 2; extracting a feature from a 3D model triangular facet having a class label and constructing a feature vector for the 3D model triangular facet having the class label, and reconstructing a feature in the constructed feature vector using a bag-of-words algorithm to obtain an initial feature corresponding to the 3D model triangular facet having the class label; training the deep CNN feature learning model using the initial feature corresponding to the 3D model triangular facet having the class label to obtain a trained deep CNN feature learning model; extracting a feature from a 3D model triangular facet having no class label and constructing a feature vector for the 3D model triangular facet having no class label, and reconstructing a feature in the constructed feature vector using the bag-of-words algorithm to obtain an initial feature corresponding to the 3D model triangular facet having no class label; determining an output feature corresponding to the 3D model triangular facet having no class label according to the trained deep CNN feature learning model and the initial feature corresponding to the 3D model triangular facet having no class label; and classifying the 3D model triangular facet having no class label according to the output feature corresponding to the 3D model triangular facet having no class label.