Concerning the problems of estimating the service time of airport flight support, and the particularity, complexity, and influence factors' uncertainty of flight support service process, an estimation model of flight support service time based on Bayesian Network (BN) was proposed. The knowledge of aviation experts and the machine learning of historical data were combined by the proposed model, and the incremental learning characteristic of BN was used to adjust the BN model dynamically, so as to make itself adapt to new conditions and constantly update the service time estimates of flight support. By using the data selected from a large domestic hub airport information system, the proposed BN model was trained via the Expectation Maximization (EM) algorithm to obtain the test results. The analysis of experimental results and model evaluation show that the proposed method can effectively estimate the service time of flight support and has higher accuracy. In addition, the sensitivity analysis demonstrates that the flight density during flight arrival time has the strongest influence on flight support service time.