ing at the problems of low recognition accuracy and slow recognition speed due to the fuzzy image of infrared ship targets, a classification algorithm based on deep convolution neural network (CNN) is proposed. By using the marker-controlled watershed segmentation algorithm, the connected regions in infrared ship image are extracted and the corresponding target positions of the original image are marked and normalized to extract the candidate regions. The improved AlexNet (a deep CNN model) is used for ship targets identification. The extracted candidate regions are sent to the improved AlexNet for feature extraction and prediction to obtain the final detection result. The marker-controlled watershed segmentation method can greatly reduce the number of candidate regions and reduce the classification time of deep CNN. The data of nearly one thousand infrared ship images are obtained by the laboratory-made infrared imaging system, and the simulation experiment on the dataset formed by its translation and scaling is performed. The simulation results show that the combination of the marker-controlled watershed segmentation algorithm and the deep CNN can effectively identify the ship targets. The proposed method has good performance and can identify infrared ship targets more quickly and accurately.