An airport surface variable slide-out time prediction method based on big data deep learning. The method comprises: obtaining historical operation data and performing data cleaning to obtain a dataset (S110); defining and quantifying a traffic condition index of a surface traffic characteristic (S120); on the basis of the dataset and the traffic condition index, analyzing and extracting a feature set influencing a surface slide-out time (S130); establishing a surface slide-out time prediction model by means of an integrated machine learning method according to the feature set (S140); and completing prediction of an airport surface slide-out time by means of the surface slide-out time prediction model (S150). Original record data of an airport is processed, modeling is performed on an airport surface traffic condition, slide-out time influence factors are analyzed and extracted, and a GBRT integrated learning model is trained, and then the slide-out time prediction model is obtained, thereby providing a data basis for management and optimization of airport operation.
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