The invention discloses a computed tomography spinal fracture auxiliary diagnosis system. The system comprises the following steps: step 1, collecting CT images, marking the CT images, and arranging the CT images into a data set; step 2, constructing a deep learning neural network based on multi-segment consistency constraint, wherein the deep learning neural network comprises positioning and marking of a spine centrum and diagnosis of a spine fracture level; step 3, pre-training the backbone network; step 4, training and testing the neural network; and step 5, inputting a CT image to be diagnosed into the trained network, and outputting a diagnosis result. The method has the advantages that: the spine image information is subjected to feature extraction through the two-stage network, a subsequent consistency analysis module is helped to pay attention to the spine centrum information, and a consistency comparison module is introduced, so that the model learning difficulty is remarkably reduced, the model performance is improved, and the diagnosis accuracy is improved.