In view of the blurred boundary and low contrast of abdominal organs as well as the different liver and tumor shapes, a feature fusion-based method for automatic segmentation of liver and tumor is proposed in this article. The method is trained in two stages. The first stage uses an improved U-Net for liver segmentation. The second stage generates the area of interest (ROI) with the result of the first stage of liver segmentation, using ROI as input for tumor segmentation, which effectively avoids the effects of non-relevant information. The proposed method is run through two stages in which channel attention is used to obtain high-frequency information between channels, and spatial attention is helpful in using image spatial and contextual information. Then, feature fusion of two output feature images is completed in jump connection section. Finally, their feature information is combined via deeply-supervised net to further improve the segmentation accuracy. In the experiment, DSC, VOE, and RVD are mainly used as the evaluation criteria in which the liver and tumor of DSC are 0.957 and 0.676, respectively, realizing an accurate segmentation of liver tumor.