On the basis of the existing residual convolutional neural networks, the weighted loss function is used to improve the discriminability of the view features of 3D models. A new view feature extraction algorithm of 3D models is proposed to optimize the residual convolutional networks. Firstly, a 3D model is rendered to obtain different views. Then, a residual network expansion module is used to increase depth of the network. Meanwhile, a weighted loss function is defined by combining the center loss function and the cross entropy loss function. As a result, it can solve the problem that the intra-class distance is less than the inter-class distance. Experiments on ModelNet datasets show that the algorithm’s performance is excellent in 3D model classification.