To effectively solve the problems of difficult to obtain data of aircraft engines and the decrease of accuracy of traditional remaining useful life prediction methods, an online prediction method of remaining useful life of engine is proposed, which mainly consists of an online model and a prediction model. The online model learns from partial running data of the engine and can be continuously updated with new running data. The purpose of the model is to keep in line with the real state of the engine. Then, the principle and implementation process of the engine RUL prediction model are analyzed in detail. Compare the performance of online prediction methods with other prediction methods. The experimental results on the dataset C-MAPSS show that the prediction performance of online methods is superior in the absence of sufficient data.