The flight control hydraulic system is a key equipment of the aircraft control system. Once it malfunctions, it will seriously affect the safe operation of the aircraft. This article takes the flight control hydraulic system - the hydraulic drive system of the aircraft door as the research object, based on its system structure and basic principles, uses the AMESim platform for simulation modeling, and conducts fault diagnosis research through fault simulation simulation. A multi-level attention mechanism-based autoencoder-multilayer perceptron fault diagnosis method is proposed that can effectively guide the maintenance and repair of equipment and improve the operational safety of the equipment. Firstly, feature extraction is performed using an autoencoder. Then, based on the multi-level attention mechanism, key feature information is further excavated. Finally, a multilayer perceptron is used as a classifier to carry out fault diagnosis. Experimental results show that compared with other traditional machine learning and deep learning models, the proposed method has a fault diagnosis accuracy of 97.5% and a false positive rate of only 1.1%, fully demonstrating the effectiveness of the proposed method.