Aiming at the bad effect of video person re-identification caused by factors such as occlusion， spatial misalignment and background clutter in cross-camera network videos， a video-based person re-identification method based on Graph Convolutional Network （GCN） and Self-Attention Graph Pooling （SAGP） was proposed. Firstly， the correlation information of different regions between frames in the video was mined through the patch relation graph modeling.In order to alleviate the problems such as occlusion and misalignment， the region features in the frame-by-frame images were optimized by using GCN. Then， the regions with low contribution to person features were removed by SAGP mechanism to avoid the interference of background clutter regions. Finally， a weighted loss function strategy was proposed， the center loss was used to optimize the classification learning results， and Online soft mining and Class-aware attention Loss （OCL） were used to solve the problem that the available samples were not fully used in the process of hard sample mining. Experimental results on MARS dataset show that compared with the sub-optimal Attribute-aware Identity-hard Triplet Loss （AITL）， the proposed method has the mean Average Precision （mAP） and Rank-1 increased by 1.3 percentage points and 2.0 percentage points. The proposed method can better utilize the spatial-temporal information in the video to extract more discriminative person features， and improve the effect of person re-identification tasks.