To address the challenges in multi-organ segmentation of abdominal CT images, such as varying organ sizes and shapes, difficulties in distinguishing boundaries between adjacent organs, and low contrast, this study proposes a feature-enhanced dual-branch multi-organ segmentation model. The model adopts an encoder-decoder architecture, with a master-slave dual-branch structure in the encoder. The master branch leverages Mamba to capture global dependencies among organs, while the slave branch employs CNN to hierarchically extract local features of multiple organs. A cascade context module is introduced to transfer detailed local features from the slave branch to the master branch. In the decoder, a multi-scale feature fusion module integrates cross-level feature information to enhance boundary sharpness in multi-organ segmentation, and a deep feature enhancement module applies a cross-attention mechanism to improve the contrast between organ foregrounds and backgrounds, mitigating the interference of background noise. Experimental results on two public datasets, Synapse and ACDC, demonstrate that the proposed model achieves notable improvements in Dice and HD95 indexes compared to recent baseline models.