Objective To study the feasibility of applying the stacking ensemble learning model to the prediction of major fault interlocking of medical linear accelerators. Methods The four fault interlocks (codes：MLC, HWFA, GWIL and UDRS) with the highest frequency of Varian 23EX linear accelerator at 119 months were retrospectively collected, and the accelerator use time (months), monthly number of treatment, monthly number of shooting fields and monthly MU were considered as the influencing factors of fault interlocking. The Stacking ensemble learning method is used to construct the prediction model of the main fault interlocking of medical linear accelerators, and the prediction accuracy and prediction performance of each base model and the ensemble learning model are evaluated by comparing the similarity, root mean square error, mean absolute value error and coefficient of determination between the fault interlocking frequency curve and the real fault interlocking frequency curve. Results Compared with the base models, the fault interlocking frequency curves of the ensemble learning model are more similar to the real fault interlocking frequency curves, and the root mean square error, mean absolute value error and coefficient of determination of the ensemble learning model are 0.41, 0.33 and 83.2% in MLC interlock fault prediction, respectively. In the prediction of HWFA interlock faults, they were 0.19, 0.17 and 74.2%, respectively. In the GFIL interlock fault prediction, they were 0.19, 0.16 and 67.9%, respectively. In the UDRS interlock fault prediction, they are 0.20, 0.17 and 71.5%, respectively. The results of each indicator were better than the single base model. Conclusions Based on the Stacking ensemble learning model, the main fault interlocking trend of linear accelerator can be predicted more accurately, which has certain application value for preventive maintenance and fault repair management of accelerator.