In order to improve the recognition rate in single-band images of ship targets with complex background, we propose a new fusion recognition method based on convolutional neural networks (CNN). This method extracts the ship target features of images in three wave bands, which are visible light, medium-wave infrared and long-wave infrared images. The model is divided into three steps. Firstly, a 6-layer CNN model is designed to extract the image features of three bands simultaneously. Secondly, a feature selection method based on mutual information is used for sorting the concatenated features according to the importance, and then the feature vectors of fixed dimension can be chosen depending on the indicator of image clarity evaluation. The dimension-reduced feature vector is regarded as the basis of target recognition. Finally, a 2-layer fully connected networks and an output layer are designed for training and regression. We build a triple-band ship target dataset for our experimental verification, which contains 6 categories of targets and more than 5000 images. The experimental results show that the recognition rate of the proposed method can reach 84.5%, which is improved significantly compared to that of the single-band recognition method.