The invention provides a zero sample sketch retrieval method based on a semantic adversarial network, which mainly solves the problems that in the prior art, the sketch intra-class variance is larger,and the visual knowledge is difficult to migrate from a known class to a non-seen class under the zero sample setting. The method comprises the steps of obtaining a training sample set, constructinga semantic adversarial network, and extracting the RGB image features through a VGG16 network, constructing a generation network to generate the RGB image features with discriminability, inputting theto-be-retrieved sketch into a semantic confrontation network to generate the semantic features, inputting the semantic features and the random Gaussian noise into the generation network to generate the RGB image features, and searching the first 200 images most similar to the RGB image features in an image retrieval library to obtain a retrieval result. According to the method, the intra-class variance of the sketch image features is reduced, the RGB image features generated according to the sketch image in each class can be ensured, the retrieval performance of zero sample sketch retrieval is improved, and the method can be used for the electronic commerce, medical diagnosis and remote sensing imaging.