The accurate segmentation of hepatic vessels is critical for the preoperative planning of liver surgeries. However, the vascular trees in the liver are complicated and highly intertwined. The hepatic vessels’ accurate segmentation is a difficult task. Due to too many sampling layers in traditional 3D-UNet model, the detailed information of hepatic blood vessels get lost during network propagation. Thus, reducing number of sampling layers reduces the expression ability of the model. In this study, based on a 3D-UNet model, a recombination recalibration model is introduced to the network to enhance the transmission of detailed information in the channel and space while suppressing the information with poor correlation. Further, attention mechanism is introduced to the model to constrain the feature graph as a whole, enabling the model to focus on the blood vessels. Finally, the sampling layers are adjusted to ensure multiscale semantic information while avoiding the loss of detailed information caused by oversampling. The best Dice Score and Sensitivity of the proposed model are 64.8% and 73.15%, respectively. The experimental results show that the improved model outperforms MPUNet, UMCT, nnU-Net, and C2FNAS-Panc in liver vascular segmentation.