Aiming at the problem of motion blur phenomenon in images taken from dynamic scenes, which leads to image quality degradation and serious loss of detail information, this paper proposes a multi-scale motion image deblurring method based on dual-domain feature fusion. First, a dual-domain feature fusion module is designed to extract spatial-domain features and frequency-domain features from blurred images in parallel using a two-branch structure, and the dual-domain features are deeply fused to improve the feature representation capability of the network model for high-frequency details. Then, a multi-scale feature aggregation module is designed to use cross-channel self-attention to aggregate the coded features of different scales of blurred images, and dynamically adjust the weights of different scales of feature maps to enhance the robustness of the model. Finally, the training loss function is improved, and the training of the network model is supervised using a joint multiscale loss function combining content loss, wavelet domain reconstruction loss and edge loss to further improve the deblurring effect. The results of comparison experiments and ablation experiments of this paper"s method on public datasets show that the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM metrics are 32.56 dB and 0.959, respectively, which are superior to other comparison methods. The experimental results show that this paper"s method can effectively improve the de-blurring effect and has good robustness.