Precisely segmenting the hippocampus from the brain is crucial for diagnosing neurodegenerative illnesses such as Alzheimer’s disease, depression, etc. In this research, we propose an enhanced hippocampus segmentation algorithm based on 3D U-Net that can significantly increase hippocampus segmentation performance. First, a dynamic convolution block is designed to extract information more comprehensively in the steps of the 3D U-Net’s encoder and decoder. In addition, an improved coordinate attention algorithm is applied in the skip connections step of the 3D U-Net to increase the weight of the hippocampus and reduce the redundancy of other unimportant location information. The algorithm proposed in this work uses soft pooling methods instead of max pooling to reduce information loss during downsampling steps. The datasets employed in this research were obtained from the MICCAI 2013 SATA Challenge (MICCAI) and the Harmonized Protocol initiative of the Alzheimer’s Disease Neuroimaging Initiative (HarP). The experimental results on the two datasets prove that the algorithm proposed in this work outperforms other commonly used segmentation algorithms. On the HarP, the dice increase by 3.52%, the mIoU increases by 2.65%, and the F1 score increases by 3.38% in contrast to the baseline. On the MICCAI, the dice, the mIoU, and the F1 score increase by 1.13%, 0.85%, and 1.08%, respectively. Overall, the proposed model outperforms other common algorithms.