In order to realize the rapid detectio n of the co-phasing error of the overall surface of the segmented mirror,a phas e measurement device was established by Zemax to simulate the tilt error detecti on process of the segmented mirror.Instead of using conventi onal phase reconstruction method,machine learning based on the combination of t he Principal Component Analysis (PCA) and the Partial Least Squares Regression ( PLSR) algorithms is investigated to extract the tip-tilt error from the intensi t y pattern.The prediction results show that when single segment is tilted,the til t angle of the 12samples is predicted,and the Root Mean Square Error (RMSE) bet ween the predicted value of the tilt angle and the true value is about 0.00029;W hen multiple segments are tilted.the RMSE of the tilt an gle can be maintained be low 0.0003.It can be seen that in both cases,the RMSE value o f the tilt angle is less than the inclination step.Therefore,machine learning algorithms can be use d to predict tilt errors with a step size of 0.0005°.Compare d with traditional methods,such as the phase diversity wavefront sensing technology,this method can greatly improve the prediction speed and reduce the complexity of the tradition al wavefront reconstruction algorithm.