Abstract: Airfoil icing affects the aerodynamic characteristics of aircraft flight, which can lead to accidents when it is serious. The prediction of ice shape parameters can effectively prevent accidents. In this paper, BP neural network is used to establish the prediction model of airfoil ice shape characteristic parameters, and k-fold cross validation is used to select the network structure, in which the meteorological and flight conditions are the inputs, and ice shape characteristic parameters such as the ice limit, the ice angle height and angle are the outputs. The experimental results show that the relative error between the predicted ice shape parameters (except for the height of the lower ice angle) and the numerical results is less than 5%, which proves that the method has a good prediction ability.