Aeroengine fault prognostic can provide basis for maintenance decisionmaking which can help to avoid catastrophic failures and minimize economic losses. According to the characteristics of aeroengine sensor data, a fault prognostic method based on bidirectional long shortterm memory (LSTM) neural network is proposed. A fault prognostic model is established, including data preprocessing, network design, training and testing. The model structure has strong generalization ability under various working conditions and faults is obtained. The model was validated using the CMAPSS data set. Compared with the RNN, GRU and LSTM time series models, the results show that the proposed Bidirectional LSTM fault prognostic model has an average error of 33.58%, which has better adaptability. The asymmetric score decreased by 71.22%, resulting in higher prediction accuracy.