The invention discloses an adversarial sample generation method based on content awareness GAN, which changes a training process on the basis of WGAN_GP, directly generates an adversarial sample witha target by inputting random noise, adds a content feature extraction part, restrains the quality of the generated sample under the condition of not influencing an attack effect, and improves the accuracy of adversarial sample generation. Content characteristics of adversarial samples can be kept unchanged as much as possible. The system comprises a generator G, a discriminator D, a target model f, a disturbance evaluation part and a feature extraction network, wherein the generator is responsible for generating a sample from random noise, the generator is trained according to a loss functionof the discriminator D, the target model f, the disturbance evaluation part and the feature extraction network, and the generator directly generates an unlimited adversarial sample from the noise. Onthe basis of the generative adversarial network, the semantic information of the concerned sample and a mode of directly generating the adversarial sample instead of a superimposed disturbance mode, direct generation of the adversarial sample of the specified target is realized by using unsupervised GAN training, the sample generation speed is increased, and the quality of the generated sample isimproved; the change of the adversarial sample in the content feature region is reduced while the high attack success rate is maintained.