Due to the structural characteristics of multi-redundancy and multi-closed loops in flight control systems, their fault propagation modes are complex, and the internal physical structure is closely coupled with system components, which poses challenges for analysis and modeling. To improve the accuracy and predictive ability of flight control system fault diagnosis, this study proposes a flight control system fault diagnosis method built on an improved bidirectional long short-term memory network. By integrating convolutional neural networks and bidirectional long short-term memory networks to extract local and temporal features of the data space, the classification and regression problems of flight control system state prediction have been solved. The results indicated that the proposed fault diagnosis algorithm had the highest recognition accuracy for the four modes. Compared with single convolutional neural networks and long short-term memory networks, the accuracy has increased by 2.11% and 1.32%, and the fault diagnosis accuracy has reached 99.49%, which could accurately identify various types of faults. The improved network proposed this time significantly improves the accuracy of flight control system fault diagnosis and reduces false alarm and missed alarm rates.