An aircraft needs to perform several flights one day, thus forming a flight chai n. After the former flight arrives the estimate departure time of next flight could be obtained if the approximate turnaround time is acquired. This paper selects several notable factors which affect the turnaround time. Firstly, the Bayesian network is used to acquire estimate turnaround time by learning the parameters through maximum likehood estimation based on historical data. Secondly, the incremental lerning property of Bayesian network is used to revise the parameters of the model based on Bayesian estimation using the increased flight data and the turnaround time is updated by the new results. The experimental data indicate that the proposed me thod has good performance on estimating the turnaround time. Finally, the s ensitivity analysis and comparison of the factors influencing turnaround time are carried out.