[Objective]This paper proposes a hierarcical classification screening method, aiming to improve the airport security system and passenger experience. [Methods] We proposed a feature deep learning method based on the civil aviation and public security databases. Then, we trained the deep neural network of three layers: time seriers, space seriers, and environmental features to obtain the joint representing feature of passenger risk factors. Finally, we generated the early warning models for passenger security check. [Results] The proposed early warning models could relieve the pressure of civil aviation security check. [Limitaions] More research is needed to examine the proposed model with data from small airports. [Conclusions] The early-warning model based on deep learning could effectively improve work efficiency of airport security checks and passanger experience.