There are many sensors for civil aviation engine health monitoring. The proper choice of sensors will directly affect the prediction effect of engine remaining useful life. A sensor selection method based on optimal feature selection is proposed and Informer algorithm is used to predict the remaining useful life, which improves the prediction accuracy. Firstly, the differential clustering algorithm is used to classify the real flight conditions, and the health factors are constructed from the degradation mechanism of civil aviation engine, and the regression tree model is established with the data of cruise stage to select important sensors. Finally, the remaining useful life of civil aviation engine is predicted based on Informer algorithm. Using NASA′s newly released civil aviation engine degradation database under real flight conditions, the experimental results show that the root mean square error of prediction results decreases by 14% and the average scoring function decreases by 29% compared with no sensor selection. Compared with the traditional selection method based on sensor degradation trend or sensor data difference, the root mean square error decreases by 10% and 8% respectively, and the average scoring function decreases by 48% and 27% respectively. Compared with CatBoost, LightGBM, XGBoost, BiLSTM and Transformer algorithms, the accuracy of the proposed remaining life prediction method is improved by 36%, 24%, 14%, 6% and 5%, respectively.