To address the issue of inefficient processing of machine vision tasks in industrial environments caused by uneven blurring in images captured in moving scenes, a motion blur image restoration algorithm based on multi-weight adaptive interaction is proposed. Firstly, a multi-strategy feature extraction module is employed to extract shallow and critical texture information from blurred images and smooth noise. Then, a dual-channel adaptive weight extraction module is proposed to capture spatial and pixel weight information from degraded images. Meanwhile, a residual semantic block is constructed to deeply mine the deep semantic information of the image and gradually compensate this 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 algorithm in the standard data set show that the SSIM and PSNR indexes in the standard data set reach 0.93 and 31.89, and each module can be well coordinated, which has significant advantages in restoring non-uniform blurred images in moving scenes.