In the Remaining Useful Life （RUL） prediction methods of aero-engine， the data at different time steps are not weighted simultaneously， including the original data and the extracted features， which leads to the problem of low accuracy of RUL prediction.Therefore， an RUL prediction method based on optimized hybrid model was proposed. Firstly， three different paths were chosen to extract features. 1） The mean value and trend coefficient of the original data were input into the fully connected network. 2） The original data were input into Bidirectional Long Short-Term Memory （Bi-LSTM） network， and the attention mechanism was used to process the obtained features. 3） The attention mechanism was used to process the original data， and the weighted features were input into Convolutional Neural Network （CNN） and Bi-LSTM network. Then， the idea of fusing multi-path features for prediction was adopted， the above-mentioned extracted features were fused and input into the fully connected network to obtain the RUL prediction result. Finally， the Company-Modular Aero-Propulsion System Simulation （C-MAPSS） datasets were used to verify the effectiveness of the method. Experimental results show that the proposed method performs well on all the four datasets. Taking FD001 dataset as an example， the Root Mean Square Error （RMSE） of the proposed method is reduced by 9.01% compared to that of Bi-LSTM network.