Video-based person re-identification （Re-ID） is to match a video track with a clipped video frame， so as to recognize the same pedestrian under different cameras. However， due to the complexity of the real scene， the collected pedestrian trajectories will have serious appearance loss and dislocation， and the traditional 3D convolution will no longer be suitable for the video pedestrian re-identification task. Therefore， a 3D feature block reconstruction model（3D-FBRM） is proposed， which uses the first feature map to align subsequent feature maps at the level of horizontal blocks. In order to fully mine the time information of the trajectory under the premise of ensuring the quality of the features， a 3D convolution kernel is added after the FBRM， and it is combined with the existing 3D ConvNets. In addition， a coarse-to-fine feature block reconstruction network（CF-FBRNet） is introduced， which not only enables the model to perform feature reconstruction in two different scales of spatial dimensions， but also further reduces computational overhead. Experiments show that the CF-FBRNet achieves state-of-the-art results on the MARS and DukeMTMC-VideoReID datasets.