Infrared imaging technology has been widely used for object tracking in military, remote sensing, security and other fields. However, thermal infrared images generally suffer from low contrast and blurry targets. Therefore, it has great importance of fusing infrared images with visible images. Compared with single-modal RGB trackers, dual-modal RGBT(RGB/Thermal infrared) trackers are more robust to illumination variation and fog. In this paper, a RGBT dual-modal siamese tracking network with feature fusion was proposed. Convolutional features extracted from the visible image and infrared image were fused to improve the appearance feature discrimination. The network can use the training data for end-to-end off-line training. Experimental results on the public RGBT234 dataset demonstrate that our tracker achieves robust and persistent tracking in complex scenarios.