The work aims to propose a pretreatment method based on stacked de-noising autoencoder for the recovery of damaged QR codes, to significantly repair the damaged QR code image on the packaging products and enhance the recognition rate, with respect to the problem that the black-and-white QR code on the shell of the packaging product is prone to be stained, eroded and damaged, and become fuzzy due to long-term wear, and easily subject to out-of-focus blur and motion blur in the process of image acquisition process, leading to the inability to complete identification requirements. Through deep leaning of stacked de-noising autoencoders of the model, the pixels interfered by the noise could be mapped to the high probability numerical point with standard data as reference according to the damaged pixel data, so as to achieve the reconstruction and restoration of the whole damaged QR code based on the pixels, thus improving the recognition rate. Through damages to the experimental QR codes, including Gaussian blur, random stain and erosion, etc., the test image set with low recognition rate or completely unable to be identified could be restored as the high-quality QR code image, significantly improving the recognition rate, featured by fast speed and good repeatability. The pretreatment method based on stacked de-noising autoencoders for the recovery of damaged QR codes can restore the damaged QR code, and be widely applied in the pretreatment before the identification of packaging products' QR code, in order to improve the recognition rate.