Abstract: Underwater acoustic target intelligent recognition is one of the important parts of intelligent underwater acoustic equipment, and deep learning is one of the important technical means to realize underwater acoustic target intelligent recognition. At present, underwater acoustic target intelligent recognition often faces the problem of insufficient training sample size caused by small data sets. Aiming at the problem of insufficient model generalization ability caused by over fitting in small data set recognition and the problem that the pattern of the two-dimensional spectrums of the input underwater acoustic signals are not unified, a target recognition method of underwater acoustic signal based on VGGish neural network model is proposed in this paper. This method takes VGGish network as the feature extractor, adds the signal preprocessing module in front of VGGish network, and designs a joint classifier based on traditional machine learning algorithm. Through the above measures, the problems of over fitting and the inconsistency of the patterns of the two-dimensional spectrums are solved. The experimental results show that this method achieves 94.397% recognition accuracy on Shipsear dataset, which is higher than the best accuracy of 90.977%achieved by traditional transfer learning method on this dataset. In the same operating environment, the model training time of this method is only about 0.5% of that of the traditional pre-training & fine-tuning method, which effectively improves the recognition accuracy and model training speed.