As Moore's law slows down and the scale of electronic transistors approaches the physical limit, the computing speed is hard to be improved. In order to solve this problem, this paper proposes a VGG16-based diffractive optical neural network (VGG16-DONN). The structure uses the optical diffractive layer as the optical front end of VGG16 and thus replaces the first electronic convolutional layer which consumes the most time during the computing in VGG16. In addition, the CelebA dataset and cat and dog datasets are classified by the proposed structure, with high accuracy of 86.34% and 88.53%, respectively, which are equivalent to that of the electronic neural network. Furthermore, based on the proposed structure, the paper constructs a VGG16-DONN method for context-dependent processing (CDP) and classifies the CelebA dataset, with an average accuracy of 83.10%, which is equivalent to that of the electronic neural network. It should be noted that the VGG16-DONN and its combination with the CDP module can address the slow computation speed of the electronic neural network by taking advantage of the fast optical computation, and they can obtain a similarly high accuracy compared with the electronic neural network, which is of great significance to image processing, medical treatment, communication, and other fields.