Image inpainting is one of an activate research topic in the domain of computer vision and computer graphics.Aiming at the problem that the traditional crack image restoration method using one-time completion restoration method does not have the ability to understand semantics,and the repair effect is not good when the semantic scene is more complex and the image defect is large,a crack image restoration based on progressive feature reasoning is proposed.This method gradually restores the image from the hole edge and strengthens the constraint on the hole center.At first,partial convolution is used to update the mask,and the update ratio is determined by calculating the mask proportion.Then,use the VGG-16 network for feature extraction,semantic attention mechanism is used to generate high-quality image features,and use the jump connection method to enhance the gradient correlation of remote distances,so as to provide multi-scale and multilevel feature information for subsequent image restoration.Finally,the recursive feature map is fused and decoded to generate a repair image.The experimental results show that the proposed method,compared with traditional image inpainting methods,can improved the peak signal-to-noise ratio of the crack image repaired for 0.5 dB—1.2 dB and produce semantic clear inpainting results.