Accurate identification of underwater targets plays a very important role in guiding aquaculture production and assisting aquaculture decision-making, and target identification accuracy and operational efficiency are the key problems affecting the in-depth application of recognition technology. In view of the application requirements of the fish identification industry, this paper takes the ResNet (Residual Neural Network) framework as the core, compares and analyses the impact of different framework structures on fish identification accuracy and effect, and determines the ResNet structure form suitable for typical breeding fish identification. Firstly, multi-camera synchronous sampling to obtain different fish images to meet the needs of a highly flexible and multi-attitude motion target sample set. Secondly, in order to improve the adaptability of samples to different backgrounds, selecting target fish images with different backgrounds to enrich the image sample set. Then, comparing the typical ResNet 18, ResNet 34, and ResNet 50 framework to analyze the overall effect of different structures on identification efficiency and identification accuracy. The rest results show that the ResNet 50 has the highest recognition accuracy at 95.47%, followed by ResNet 34 and 95.03%, but the ResNet 50 recognition efficiency is 20.43% lower than ResNet 34. Considering the recognition accuracy and recognition efficiency, ResNet 34 is more suitable for the recognition classification of fish images with a large sample size.