The robustness of convolutional neural network （CNN） models is usually improved by deepening the number of network layers to ensure the accuracy of the results. However， increasing the number of network layers will make the network more complex and occupy more space. This paper proposes an improved CNN modeling method based on human visual features. Through the CNN， the structural features of human vision are fused to improve the robustness of the network against noise without increasing the number of layers or affecting the original accuracy of the model. The experimental results on the Cifar10 dataset show that the classification accuracy of the image inserted into the proposed VVNet is almost the same as that of the original network， and the classification accuracy is improved by approximately 10% in the case of image destruction. Compared with the original deep learning network， the network based on human visual system structure can effectively enhance the robustness of the network while maintaining the original accuracy.