Considering of the quantifiable influencing factors of taxi-out time, the prediction model of departure flight estimated taxi-out time based on BP was constructed. Then genetic algorithm (GA) is used to optimize the weights and thresholds of BP neural network, and an aircraft push-out control strategy based on taxi-out time prediction is proposed in this paper. Finally, the prediction model and control strategy are validated by two weeks' actual operation data of a hub airport in the Central and South China. The results indicate that: (1) estimated taxi-out time has strong correlation with airport traffic flow, moderate correlation with average taxi-out time and weak correlation with taxi distance and number of turns; (2) The prediction accuracy of BP neural network optimized by GA is increased by 14% in ± 60s, 10% in ±180s and 5% in ±300s. And the mean absolute error percentage of the prediction results increased by 1.87%, the mean absolute error decreased by 3.58s, and the root mean square error decreased by 32.45s. (3) The calculated off block time based on taxi-out time prediction is 68s later than the actual off block time. It provides a new way to improve the operation efficiency and collaborative decision-making ability of large hub airports.