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Aiming at the low identification accuracy of cows with solid color or less pattern in pattern-based individual identification of cows, an individual identification method was proposed based on cow gait features. Firstly, the backbone network of DeepLabv3+ semantic segmentation algorithm was replaced by MobileNetv2 network. The channel and space based CBAM attention mechanism was introduced into this segmentation algorithm. The improved model was used to segment the silhouette of the cow. Then the 3D convolutional neural network (3D CNN) and the bidirectional long short-term memory network (BiLSTM) were constructed as the 3D CNN-BiLSTM network. The adaptive temporal feature aggregation module (ATFA) was further integrated into the above network to generate the 3D CNN-BiLSTM-ATFA cow individual identification model. Finally, individual identification experiments were conducted on a total of 1242 video datasets from 30 cows. The results showed that the MPA, MIOU and Accuracy of the improved DeepLabv3+ algorithm were 99.02%, 97.18% and 99.71%, respectively. Individual recognition was optimal when r3d_18 was used as the backbone network of 3D CNN-BiLSTM-ATFA. The average accuracy, sensitivity and precision of individual identification based on cow gait were 94.58%, 93.47% and 95.94%, respectively. Individual identification experiments with weighted feature fusion for torso and legs showed that identification accuracy can be further improved. Lameness in dairy cows had a significant effect on gait identification, the individual identification accuracies were 89.39% and 92.61% for cows that changed from healthy to lame and cows that remained lame during the experiment, respectively. The results can provide technical reference for intelligent individual identification of dairy cows.