Owing to the high reflectivity of the water surface and the influence of edge features such as ripples, the traditional water surface target recognition algorithm is unable to appropriately identify the target. To this end, a water surface target recognition algorithm based on deep learning is proposed herein. First, a large number of target samples are collected and labeled, then the parameters and network structure of the algorithm are optimized based on the principle of the YOLOv3 (You Only Look Once v3) algorithm. Then, the target samples are trained using the deep convolutional neural network. The data enhancement of the target sample is conducted to adapt to different environments to improve the robustness of the proposed algorithm, and the phase correlation waterfront recognition algorithm is used to improve the recognition speed. Finally, the weight file obtained from the network structure training of the proposed algorithm is used to establish a surface target recognition system, which can achieve a higher recognition rate. Experimental results verify the effectiveness and robustness of the proposed algorithm and can provide a reference for future research on surface target recognition.