As the main power source for aircrafts, the reliability of an aeroengine is critical for ensuring the safety of aircrafts. remaining useful life (RUL) prediction is of great importance for improving the availability of an aero engine and reducing its life cycle cost. For the problem of the shortcomings of existing estimation algorithms in the extraction of multi-dimensional data features, this paper proposes an attention-based CNN-BiLSTM model for RUL estimation. This model using CNN layers to extra feature and BILSTM network can capture the short-term and long-term dependencies of the extracted feature. Afterwards, attention mechanism layer is used to highlight the important features in order to improve model performance. To evaluate the effectiveness of our approach, experiments are carried out on CMAPSS datasets and its result shows that the performance of the proposed approach is superior to other traditional approaches.