Abstract: The present invention discloses a method for reconstructing incomplete data of X-ray absorption contrast computed tomography (CT) based on deep learning (DL). The method includes the following steps: using a filtered back projection (FBP) algorithm to obtain an initial reconstructed image; forward projecting the initial reconstructed image to obtain artifact-contaminated complete projection sequences; using a DL technique to process the artifact-contaminated projection sequences to obtain artifact-free projection sequences; using the FBP algorithm to reconstruct the artifact-free projection sequences to obtain a final reconstructed image. Compared with the traditional incomplete data reconstruction methods, the examples of the present invention feature a simpler calculation process, fewer parameters to be manually set, a faster calculation speed and higher image quality.