Objective：To explore the influence of ¹⁸F-FDG activity on PET/CT image quality and establish a PET image quality restoration method to achieve radiation protection for patients. Methods：A deep learning network SwinUNetR -GAN for restoring the quality of low - activity PET images based on the global feature recognition of Swin Transformer was established. The images of 124 patients who underwent ¹⁸F-FDG PET/CT examinations in 2024 were randomly selected to explore the law of PET image quality degradation under different activities of ¹⁸F-FDG and the quality of low-activity PET images was achieved based on the SwinUNetR- GAN network. Results：As the activity of ¹⁸F-FDG decreases，the mean and maximum values of the standard uptake value（SUV） （SUVmean and SUVmax）in normal tissues and tumor lesions in the PET images show an increasing trend. When the activity of 18F - FDG is reduced to 10% of the current clinical activity，the SUVmean of the tumor lesion increases by about 1.1 times，while the SUVmax increases by about 2.0 times. In addition，the use of the SwinUNetR-GAN network can reduce the noise of 10% activity PET images，The absolute deviation decreased from 0.21 to 0.15，and the relative deviation could be reduced from 0.33 to about 0.25. Conclusion：This study clarifies the change law of quantitative parameters of normal tissues and tumor tissues in patient PET images under activity of ¹⁸F-FDG and then，SwinUNetR -GAN network dedicated to low 1¹⁸F-FDG activity PET image quality restoration is proposed，which could achieve the disease diagnosis performance of PET images while reducing the radiation dose deposited in patient.