Abstract: Mine water inrush video recognition is a key component in intelligent mine construction. By recognizing the dynamic evolution of water inrush from none to some and from small to large, it helps prevent the water volume from exceeding the mine's drainage capacity and turning into a water hazard. Therefore, a video segmentation model based on the Multi-channel Residual Attention mechanism and U²Net (MRAU) was proposed to identify the evolution process of water inrush. First, the U²Net network model was improved based on the Convolutional Block Attention Module (CBAM) to enhance feature extraction. Then, through multi-channel residual preprocessing, the dynamic features of water flow were distinguished from the static background, and the processed results were input into the model as an attention mechanism to reinforce the learning of water flow features. In addition, intermediate frame masks were used as labels for multi-frame fusion learning, further enhancing the network’s ability to recognize the dynamic features of water flow. Finally, by learning the water flow features in different scenarios, the model effectively recognizes the dynamic changes of water inrush in unknown scenarios. Comparative experiments with Deeplab, LRASPP, FCN, and U²Net network models, using Dice and IoU as evaluation metrics, show that the Dice and IoU of the MRAU model reach 92.88% and 87.51%, respectively, which represents improvements of 4.71% and 7.41% over the baseline U²Net network. When tested in unknown water inrush scenarios, the MRAU model achieves Dice and IoU scores of 86.75% and 80.23%. Compared to other models, MRAU achieves the highest recognition accuracy, demonstrating stronger generalization capabilities in recognizing water flow features across different scenarios. Moreover, MRAU can accurately monitor the dynamic evolution of water inrush from small to large. Finally, simulations of water inrush scenarios in underground environments further verify the practical utility of the MRAU model in real-world production, providing an effective technical solution for mine water hazard monitoring.