Aiming that QR codes on shell packaging products in the process of collecting, motion blur, out-of-focus blur caused by long exposure and wear the fuzzy and environmental factors such as noise, QR code can't identify the problem, the paper aims to put forward a kind of QR codes to fuzzy algorithm based on generative adversarial network. Adversarial network generated by deep learning model had strong fitting and estimation ability on fuzzy core and environmental noise. Deep characteristics and the gap between fuzzy QR code images and real images were extracted. Through constant iterative against generator and discriminator, the generator had the ability of deblurring QR codes of different fuzzy degrees in the dataset. The generator could estimate the fuzzy core and environmental noise well and deblur multiple kinds of QR codes. It was featured with good effect, fast treatment, and high recognition rate on deblurring QR code images. The QR code deblur algorithm based on generative adversarial network can be widely used in the preprocessing of QR codes on the packaging product case and improve the scanning recognition rate.