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In order to study the problem of departing flight recovery considering airline flight fairness, firstly, according to the historical data of an airport, the influence of uncertain factors on airport capacity was studied, and the influence of uncertain factors such as weather on the turnaround time during the flight recovery process was obtained through Bayesian network. Then, a multi-objective flight recovery scheduling model was constructed to minimize total delay time and determine the fairness of airlines based on the Gini coefficient. In addition, the disturbance of the uncertainty factor to the airport capacity and turnaround time was added to the model. Finally, the NSGA-Ⅱ algorithm was used to analyze the flight data of a domestic airport and compare it with the first come first service method. The results show that the total delay time is reduced by 37. 2%, the airline fairness is improved from 0. 440 to 0. 202, and the built model has better absorption performance of disturbance, which verifies the feasibility of the model and algorithm. [ABSTRACT FROM AUTHOR]