In order to solve the problems of complex mechanism and high dimension of state detection data in turbofan engine degradation process,a prediction method of remaining useful life (RUL) of turbofan engine was proposed based on the combination of random forest (RF) and self-attention (SA) deep gated recurrent units (DGRU).RF algorithm was used to determine the importance threshold value to achieve feature screening.The selected features were input into DGRU-SA module,and the hidden information between the relevant features and the target value was mined through the multi-layer GRU,and the SA was used to add different weights to the hidden information.Finally,the full connection layer was used to output the prediction results.Experimental verification was carried out with CMAPSS data set.The results show that the proposed fusion model has less error and good prediction accuracy and stability compared with the traditional multi-layer perceptron,convolutional neural network,long short-term memory.