In order to solve the problem of low recognition accuracy of traditional fault diagnosis methods in aero engine fault diagnosis, a fault diagnosis model of aero engine fault diagnosis based on gray Wolf optimization algorithm, convolutional neural network, bidirectional long and short time memory network, and attention mechanism (GGO-CNN-BILSTM-attention) was proposed. Firstly, wavelet soft threshold is used to reduce noise and normalize high-dimensional aero engine sensor data to fully explore the real change characteristics of multidimensional data. Secondly, a fault diagnosis model based on CNN-Bilstm-attention is constructed, and a Convolutional Neural Network (CNN) is used for feature extraction and fusion to generate several mappings. The data mapping is input into Bidirectional Long Short-Term Memory (BiLSTM) for training, to capture the time dependence in time series data, and output fault identification results. Finally, Grey Wolf Optimization (GWO) was used to select hyperparameter optimization to improve the model performance. The experimental results of aero engine fault diagnosis show that the accuracy of the proposed model is 93.38% and the AUC value is 93.14% in the aero engine fault diagnosis experiment, which verifies that the proposed model has high performance and robustness under noisy conditions.