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In this paper, a turbulence model based on deep neural network is developedfor turbulent flow around airfoil at high Reynolds numbers. According to thedata got from the Spalart-Allmaras (SA) turbulence model, we build a neuralnetwork model that maps flow features to eddy viscosity. The model is then usedto replace the SA turbulence model to mutually couple with the CFD solver. Webuild this suitable data-driven turbulence model mainly from the inputs,outputs features and loss function of the model. A feature selection methodbased on feature importance is also implemented. The results show that thisfeature selection method can effectively remove redundant features. The modelbased on the new input features has better accuracy and stability in mutualcoupling with the CFD solver. The force coefficient obtained from solution canmatch the sample data well. The developed model also shows stronggeneralization at different inflow condition (angle of attack, Mach number,Reynolds number and airfoil).

High Reynolds number airfoil turbulence modeling method based on machine learning technique