In this study, a face image restoration method based on latent feature reconstruction and attention mechanism was proposed to address the problem that existing image restoration methods cannot effectively separate structure and texture information, and the restoration results often show artifacts such as blurred boundaries and distorted structures. The face image restoration method was divided into two stages. In the first stage, the style vectors were extracted through the structural reconstruction network and divided into three groups of coarse-scale features, medium-scale features, and fine features according to the principles described by StyleGAN, which were inserted into the pre-trained StyleGAN generator to produce initial restoration results. In the second stage, by building a texture generation network and using a contextual attention mechanism, the attention score was calculated by the attention calculation module, and the attention transfer module filled in the corresponding missing regions in the lower-level feature images based on the higher level feature images and the attention scores to refine the initial face restoration results from the previous stage. Trained and tested on the CelebA-HQ dataset, the method in this paper outperformed existing methods in both quantitative and qualitative analysis. Thus, the face image restoration method based on latent feature reconstruction and attention mechanism can effectively repair defective face images, greatly reducing the problems of excessive smooth boundaries and the presence of texture artifacts.