The existing face image restoration methods often generate images with structural incoordination and contextual semantic incoherence when dealing with large-area occlusion，and are unable to accurately restore facial details. Aiming at this problem，a multi-stage face image restoration algorithm based on contrastive language-image pre-training （CLIP） and attention mechanism optimization was proposed. First of all，the initial facial recovery was carried out through the codec structure. Then，local feature processing was carried out for the mask-covered areas，and aggregated contextual transformations（AOT） blocks were introduced into the local detail network to enhance the detail reconstruction effect of the facial edges and covered areas. Finally，the pre-trained CLIP model was embedded into the global encoder to capture the overall fine-grained details of the image，and the global facial information was integrated through the attention mechanism to ensure that the restored image meets the requirements of high-quality restoration in terms of global consistency and local details. This method was compared with various algorithms on multiple datasets. The experimental results show that the proposed method performs outstandingly in both subjective and objective evaluations，achieving a better balance between integrating computational efficiency and improving the quality of restoration.