3D medical image registration algorithm is widely used in scientific research, follow-up and other medical fields, so it is of great significance to improve its registration accuracy. Aiming at the problem of medical image registration, a V-shape network based on V-Net(VV-Net) is proposed. The registration model can be trained end-to-end by stacking V-Net. Specifically, the moving image is distorted by two V-Nets in turn, and the additional V-Net is used to provide supplementary information for the first two V-Nets to form a V-shaped network, so that the moving image can be better aligned with the fixed image. At the same time, the depth supervision auxiliary branch is added to the proposed model to prevent over fitting. The accuracy of registration is improved by using the progressive registration and information supplement. The performance of the model is evaluated by ADNI、ABIDE、ADHD200 and OASIS data sets. Compared with affine transformation, symmetric normalization (SyN) and VoxelMorph, the proposed registration method achieves 24.7%, 13.2% and 1.3% accuracy improvements, respectively. The experimental results show that VV-Net has achieved good results in the field of medical image registration.