## 1 Introduction
These defects result in the weak generalization ability of the underwater acoustic target recognition algorithm based on deep learning and limit the practicality of the underwater acoustic target recognition algorithm. For example, in the case of a limited amount of training data, the recognition of underwater acoustic targets of the same voyage shows high accuracy, but the accuracy of underwater acoustic targets of different voyages is seriously reduced. Similarly, many fields cannot directly use deep learning to model simply because of data attributes. Researchers try to use domain knowledge or optimization-based strategies to assist modeling and have made some progress [31; 32; 33; 34]. In this paper, to solve the problem of generalization modeling using deep learning, the design of an interpretable algorithm and the deployment of a transfer learning method are considered. In terms of interpretable algorithm design, instead of piling up the network structures, this paper proposes a feature extraction module based on a neural network, which integrates key technologies of signal processing and neural networks, such as digital filtering technology, time-frequency analysis technology and attention mechanisms. Using neural network learning, we try to optimize the design of intelligent algorithms from an interpretable perspective. In particular, the neural network-based feature extraction module receives a one-dimensional signal from the time domain and applies a neural network to realize digital filtering and time-frequency feature extraction. The frequency band suitable for the current classification task is mined from the signal by the feature extraction module, and the frequency response of the neural network can be output in real-time. For the deployment of transfer learning methods, inspired by image recognition, researchers train the neural network model on the large-scale dataset of ImageNet [35] and transfer the trained model to downstream image processing tasks, achieving good results. However, in the field of audio pattern recognition, the performance of pre-trained audio pattern recognition systems on large-scale datasets is still a problem yet to be solved. For underwater acoustic target recognition, the feasibility of the pre-trained model needs to be discussed and verified, especially when the pre-training task is not related to the underwater acoustic target recognition. The underwater sound recognition performance of the pre-trained model trained on large-scale audio pattern recognition data is verified in this paper. The following sections are divided into four parts. Section 2 describes the design of the feature extraction module of an attention-based neural network (FEM-ATNN), including the design of a time domain filter based on a single convolution kernel and the design of the Fourier transform module based on the attention mechanism. Section 3 describes the selection of the network model and the validation of model-based transfer learning. Section 4 discusses the feasibility and effectiveness of the proposed method in underwater acoustic target classification tasks and conducts various experiments. Finally, the full text is summarized in Section 5.