Abstract: Traditional freshness detection methods for aquatic products have problems such as great sample damage, trivial operation steps, low detection accuracy and efficiency. To solve these problems, in order to efficiently and accurately identify the freshness of small yellow croaker (Larimichthys polyactis), we proposed a freshness recognition model based on an improved DenseNet network. Firstly, we introduced the SENet attention mechanism module into each dense block module in the DenseNet network structure to achieve feature channel feature recalibration, enhance the network's extraction of current beneficial features, and eliminate irrelevant features. Secondly, we improved the first layer of the convolutional layer to enhance the network's non-linear ability and feature representation ability. To prevent the phenomenon of gradient vanishing during the training process, we used the PReLU activation function instead of the ReLU activation function of the original network. Finally, we conducted comparative experiments with the DenseNet network model and other classic neural network models. The experimental results show that the FishNet model based on transfer learning constructed in this paper has a recognition accuracy of 91.53% on the built L. polyactis freshness dataset. The model has high recognition accuracy and strong robustness, achieving efficient and accurate recognition of aquatic product freshness detection, and providing references for the development of intelligent freshness recognition systems.