2D/3D medical image registration is a key technology in 3D real-time navigation of orthopedic surgery. However， the traditional 2D/3D registration methods based on optimization iteration require multiple iterative calculations， which cannot meet the requirements of doctors for real-time registration during surgery. To solve this problem， a pose regression network based on autoencoder was proposed. In this network， the geometric pose information was captured through hidden space decoding， thereby quickly regressing the 3D pose of preoperative spine pose corresponding to the intraoperative X-ray image， and the final registration image was generated through reprojection. By introducing new loss functions， the model was constrained by “Rough to Fine” combined registration method to ensure the accuracy of pose regression. In CTSpine1K spine dataset， 100 CT scan image sets were extracted for 10-fold cross-validation. Experimental results show that the registration result image generated by the proposed model has the Mean Absolute Error （MAE） with the X-ray image of 0.04， the mean Target Registration Error （mTRE） with the X-ray image of 1.16 mm， and the single frame consumption time of 1.7 s. Compared to the traditional optimization based method， the proposed model has registration time greatly shortened. Compared with the learning-based method， this model ensures a high registration accuracy with quick registration. Therefore， the proposed model can meet the requirements of intraoperative real-time high-precision registration.