In coastal surveillance video images of ship target recognition problem, the speed and accuracy of traditional detection methods need to be further improved. The deep learning method has been able to recognize ship targets, but the model network structure is complex and there are interaction effects of hyper parameters, the model training time is long, and the identification accuracy is often limited by the selection of training samples and hyper parameters. In the course of CNN model training of YOLO algorithm, the concept of uniform design is planned to be adopted to coordinate optimization of the level values of multiple influencing factors, so as to reduce the blindness of adjusting the parameters of deep learning algorithm according to subjective experience. After analyzing the impact of clarity and manual annotation, the scene, sample ratio of training set, training algorithm and training rounds were taken as the influencing factor. Differentiated level values were set and the deep learning model was trained by uniform design method. Using the value of AP to compare and analyze the empirical method and uniform design method. Taking three water areas of Huangpu River in Shanghai as example, the results show that: Among the 9 groups of schemes designed by the general empirical method, the optimal value of AP is 0.84. Among the 6 group of schemes generated by the uniform design method, when YOLOv5x algorithm is selected, the sample proportion is 90% and 100 training rounds, the accuracy of deep learning model is high, and the optimal AP value is 0.91. It is concluded that the effectiveness of the uniform design method in hyper parameter training of deep learning model.