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Currently, the basic UNet model cannot effectively meet the demand for CT images with metal artifacts reduction, the simple structure of UNet cannot extract sufficiently accurate information about the effective structure and details, and the deep convolution does not sufficiently use the information of low-level features. Based on the above problems, a metal artifact training dataset based on actual CT images and a metal artifact removal network with attention gates based on UNet is constructed. The network adopts attention gates to apply attention weights to the information at low and high levels and feeds back to the feature decoding structure using a jump connection mechanism to improve the quality of the generated CT images. The final CT image Experience results show that the method yields CT images with better removal of stripe and band artifacts compared to ADN,cGANMAR,UNet,CNNMAR,CycleGAN models.