Abstract: The flight operation risk is equal to the occurrence probability multiplied by the severity of the consequences. Flight operation risks include many types, forms, and numbers, and they frequently change with conditions. In the face of this complex system, through principle analysis, the risk formation mechanism research, and the spreading process, a scientific risk management and control method can be constructed. Based on the risk management technology, an informative and automated management control system can be developed and applied. The overall safety level of flight operations will be effectively improved. To analyze and study the flight operations risk propagation and then effectively control flight safety based on the complex network theory, 29 terminal factors were selected as network nodes according to the Civil Aviation Administration’s advisory notice, initially including the flight cabin crew, civil aviation aircraft, and operating environment. Civil aviation safety monitoring records from 2009 to 2014 were counted, and an undirected network was constructed based on node relationships. The relationships and occurrence probability between the nodes were counted, and a directed and weighted network was constructed. The concepts of improved infection rate and improved recovery rate were introduced, and an improved susceptible-infected-recovered (SIR) model suitable for flight operation risks was proposed. Finally, the initial infection range was clearly defined, and a multi-parameter control method was adopted. For directed networks, large-scale propagation and control simulations were calculated. The results indicate that the average shortest path of the directed network was 1.788, which belonged to the small-world network. The directed network infection node decreased to 37.4% with conventional control measures. After controlling top three or four nodes of the entry degree value sequence, the infected nodes peak drop rate was the biggest, as high as 50.6%/58.1%, the risk spread in the network was significantly suppressed. The results confirm that controlling nodes based on the entry degree value is the most effective method to suppress risk propagation in the directed and weighted network.