In the process of pedicle screw insertion surgery assisted by spinal surgery robot, the surgeon needs to spend a lot of time in searching and determining the location suitable for pedicle screw operation. In order to improve the operation efficiency, a rapid method based on machine learning was investigated to realize efficient planning of robot-assisted pedicle screw insertion. In the proposed method, the convolution neural network that commonly used in artificial intelligence research was introduced based on training set of spine computed tomography images. Firstly, the neural network model was set up to determine the adjustment parameters between layers in the network. Then the feature extraction and classification of sample image were carried out. The cross validation method was used to train sample data to verify the correctness of convolution neural network model. Finally, the method of machine learning was used to identify the computed tomography images of patients that suitable for the location of pedicle screw implant. With this proposed method, surgeons only need to complete the final fine planning based on the area of safety constraint, and efficiency of operation can be improved distinctly.