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We propose a novel deep learning model, VAN\u2010ConvLSTM, for rapid and accurate prediction of UAV downwash airflow. Unlike conventional ConvLSTM\u2010based frameworks, which struggle with modeling long\u2010range dependencies and detailed spatial variations, our model introduces a visual attention unit (VAN) to enhance spatiotemporal sensitivity. The model architecture combines a convolutional encoder for spatial feature extraction, a VAN module for attention\u2010guided temporal modeling, and a ConvLSTM decoder for sequence generation. This synergistic design improves both the accuracy and interpretability of airflow prediction. Experimental results show that the VAN\u2010ConvLSTM model achieves an SSIM score of 0.96, demonstrating high consistency with CFD simulations. Compared to baseline methods, our model reduces error while improving stability and spatial fidelity. Ablation studies further validate the individual contributions of VAN and ConvLSTM modules. 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