To super-resolve low-resolution(LR) face image suffering from strong noise and fuzzy interference, we propose a noisy face super-resolution(FSR) method based on multi-level latent space information constraints. Firstly, we design a feature distillation network to extract effective facial information, which exploits a statistical anti-interference model and a latent contrast algorithm to remove invalid information such as noise. Then, we design a face reconstruction network, which utilizes the extracted face features to reconstruct high-resolution(HR) face images. Finally, we deploy a face identity embedding model and a discrete wavelet transform model to further supervise the reconstruction of identity information and spatial structure from the hypersphere identity metric space and wavelet domain respectively. The experimental results show that the proposed method not only removes the noise from face in the high noise environment, but also improves the resolution of the face image effectively, which obtains higher PSNR, SSIM, and good practicability.