Aiming at the airway fault problems occurring during the operation of aero-engine, an aero-engine airway fault diagnosis model based on Convolutional Neural Network (CNN) combined with Bald Eagle Search Algorithm (BES) Optimized Extreme Learning Machine (ELM) is proposed. The aero-engine airway data are learned by CNN and the fault features hidden in the data are extracted, the BES algorithm is introduced to optimize the weights and biases of the ELM, and the optimized ELM is used to classify the abstract features extracted by the CNN, so as to achieve the purpose of fault diagnosis. The experimental results show that the CNN-BES-ELM-based model achieves an average accuracy of 97.80%, which is 2.7%, 5.4%and 7.35% higher than that of CNN-ELM, CNN and ELM, respectively, and compared with commonly used deep learning models such as Deep Belief Network (DBN) and Stacked Auto Encoder (SAE), the accuracy is improved by 5.4% and 3.4%; and still retains more than 90% accuracy in noise environments such as random noise, Gaussian noise and pretzel noise, which overall shows good diagnostic performance, generalization ability and noise immunity, and provides a theoretical basis for its practical application in aero-engine airway fault diagnosis.