An improved multilayer progressive guided face-image inpainting network is proposed to solve problems such as artifacts and incongruent facial contours after face-image inpainting. The network adopts an encoding‍-decoding structure comprising structure-complement, texture-generation, and main branches, and gradually guides the generation of structure and texture features among different branches. A feature-extraction module is introduced to enhance the connection between different branches when the feature transfer is carried out in different branches. Additionally, a feature-enhancing attention mechanism is designed to strengthen the semantic relationship between channel and spatial dimensions. Finally, the output features of different branches are passed on to the context aggregation module such that the inpainting images become more similar to the actual images. Experimental results show that, compared with PDGG-Net (Progressive Decoder and Gradient Guidance Network), the proposed network in the CelebA-HQ dataset presents average improvements of 0.003 and 0.13 dB in terms of the SSIM and PSNR, respectively. To prevent overfitting, multi-dataset joint training and fine-tuning are performed in the sparse profile dataset, which improves the SSIM and PSNR by 0.003 and 0.29 dB on average, respectively, compared with the results of direct training using the profile dataset.