To address the issue of inefficient processing of machine vision tasks in industrial environments caused by non-uniform blur in images captured in moving scenes,this paper proposes a motion blurred image restoration approach based on multi-weight adaptive interaction.Firstly,a multi-strategy feature extraction module is employed to extract shallow and critical texture information from blurred images while smoothing noise.Meanwhile,a residual semantic block is constructed to deeply mine the deep semantic information of the images.Secondly,a dual-channel adaptive weight extraction module is introduced to capture spatial and pixel weight information from degraded images and gradually incorporate these information into the network.Finally,a weighted feature fusion module is designed to fuse the multi-spatial weighted features extracted by the network,and multiple loss functions are combined to further improve image quality.The subjective,objective and ablation experimental results of the proposed approach on standard datasets show that the SSIM and PSNR indices reach 0.93 and 31.89,respectively.The modules work well in coordination,exhibiting significant advantages in restoring non-uniform blurred images in moving scenes.