Blind face restoration is to recover high quality face from unknown degradation， and the ill-posed problem often results in local texture missing or mismatched facial components for restored images， therefore a degraded blind face restoration algorithm based on feature fusion and embedding optimization is proposed. By extracting face prior features from degraded inputs， using multi-headed cross-attention for feature interaction fusion and global context modeling， embedding facial priors into the latent space of pre-trained generative networks， and carrying out optimization based on loss functions， local textures lost or damaged due to degradation are repaired to achieve a balance between realism and fidelity. Numerical experiments are conducted on three real degraded datasets， which outperform existing methods in terms of objective metrics and subjective quality， and the final ablation experiments validate the effectiveness of the degraded blind face restoration algorithm.