In order to solve the problems such as lack of large-scale aquatic animal data set, heavy workload of artificial data collection and limited feature enhancement of data by traditional data enhancement methods, a data enhancement method based on deep convolutional generative adversarial network was proposed for aquatic animal image recognition. First of all, deep convolutional generative adversarial network (DCGAN) was used to enhance the sample data, and then three training models: VGG16, InceptionV3 and ResNet50 were used to train and identify the samples by fine tuning. The results show that the classification accuracy of the proposed method on the aquatic animal data set can be improved by 9.8%, 2.7% and 1.2% respectively compared with the non-generated data enhancement method. The experiment proves that DCGAN can effectively enhance the image data of aquatic animals and improve the accuracy of deep neural network model in classifying aquatic animal images.