Abstract: Underwater acoustic target recognition (UATR) based on radiated noise is one of the main passive sonar applications. To further improve the classification accuracy of underwater target with small sample, a novel method based on dual attention networks (DAN) and a multiresolution convolutional neural network (DAN-MCNN) is proposed. Firstly, the three-dimensional (3D) aggregated features are designed by the multi-class feature subsets, which are composed of MFCC, Log-Mel spectrogram, chroma, spectral contrast, and tonnetz. Then, based on the frequency perception mechanism of the human ear and the auditory attention mechanism, a multi-resolution pooling and convolution scheme is adopted to construct the MCNN architecture, which can better adapt to the time-frequency structure of the 3D aggregated characteristics. Besides, the DAN module is used to capture the global dependence and local characteristics of samples. An exponentially weighted categorical cross-entropy (EWCE) is taken as the loss function to improve the recognition rate of categories with fewer samples. The experimental results show that the proposed approach achieves average recognition accuracy of 95.5% in the ShipsEar dataset, which is the best classification result.