The invention discloses a picture generation method based on depth learning and a generative adversarial network. The method comprises steps that (1), a picture database is established, multiple real pictures are collected and are further classified and marked, and each picture has a unique class label k corresponding to the each picture; (2), the generation network G is constructed, a vector combined by a random noise signal z and the class label k is inputted to the generation network G, and generated data is taken as input of a discrimination network D; (3), the discrimination network D is constructed, and a loss function of the discrimination network D comprises a first loss function used for determining true and false pictures and a second loss function used for determining picture classes; (4), the generation network is trained; (5), needed pictures are generated, the random noise signal z and the class label k are inputted to the generation network G trained in the step (4) to acquire pictures in a designated class. The method is advantaged in that not only can the pictures can be generated, but also the designated generation picture classes can be further realized.