Aiming at the problems that traditional aquatic animal image recognition methods have complex steps, low accuracy and poor generalization, and it is difficult to develop Deep Convolutional Neural Network (DCNN) model, a method based on parameter transfer strategy using fine-tune to retrain pre-trained model was proposed. Firstly, the image was preprocessed by data enhancement and so on. Secondly, on the basis of modifying the source model's fully connected classification layer, the weights of high-level convolution modules were set to be trained for adaptive adjustment. Finally, using training time and recognition accuracy on validation set as the evaluation indexes, the performance experiments were conducted on various network structures and different proportion of trainable parameters. The experimental results show that the highest retrained model classification accuracy can reach 97.4%, 20 percentage points higher than the source model, the ideal performance can be obtained when the proportion of trainable parameters is around 75%. It is proved that the fine-tune method can obtain a deep neural network image classification model with good performance under low-cost development condition.