Recently, with the rapid development of underwater resource exploration and underwater activities, Underwater Acoustic (UA) target recognition has become crucial in marine resource exploration. However, traditional underwater acoustic recognition systems face challenges such as low energy efficiency, poor accuracy, and slow response times. Systems for UA target recognition using deep learning networks have garnered widespread attention. Convolutional neural network (CNN) consumes significant computational resources and energy during convolution operations, which exacerbates the issues of energy consumption and complicates edge deployment. This paper explores a high-energy-efficiency UA target recognition system. Based on the DenseNet CNN, the system uses fine-grained pruning for sparsification and sparse convolution computations. The UA target recognition CNN was deployed on FPGAs and chips to achieve low-power recognition. Using the Noise-disturbed ShipsEar dataset, the system reaches a recognition accuracy of 98.73% at 0dB signal-to-noise ratio (SNR). After 50% fine-grained pruning, the accuracy is 96.11%. The circuit prototype on FPGA shows that the circuit achieves an accuracy of 95% at 0dB SNR. This work implements the circuit design and layout of the UA target recognition chip based on a 65nm CMOS process. DC synthesis results show the power consumption is 90.82mW, and the single-target recognition time is 7.81ns.