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Computer vision technology is widely applied in fish individual identification. Nevertheless, there are some problems such as small fish targets, occlusion of objects and light interference in videos and images. Some fish identification methods based on color, shape and texture also exit complicated calculations in feature extraction, such as nonmigration of features will result in low recognition accuracy and poor classification. With the help of analysis of image feature extraction of the existing VGG16 convolutional neural network model, the FTVGG16 convolutional neural network (Finetuning VGG16 convolutional neural network) was designed. As it was known, the basic deep learning tool used in this work was convolutional neural networks. The FTVGG16 convolutional neural network was composed of convolutional layers, batch normalization layers, pooling layers, Dropout layers, fully connected layers and softmax layers. The experimental results showed that the average recognition accuracy of the FTVGG16 model for fish was about 97.66%, and the average recognition rate of some fishes could reach 99.43%. It had high recognition accuracy and robustness in pictures with small fish targets and strong background interference. It could be operated through an appropriate, easytouse, and userfriendly web application for the specific case of fish identification.