Ice accretion affects flight safety severely. Intelligent ice accretion prediction is an important basis and support for the intelligent anti-icing and de-icing system design and safety design of aircraft. To solve the problem that there are multiple values of ice thickness in the normal direction of the same position of the airfoil surface with complex ice shape, a graphical ice accretion prediction method based on the transposed convolution neural network was proposed. The corresponding neural network structure, loss function and data specification of the prediction model were designed. The input of the prediction model was the data from flight and atmospheric conditions which directly affect ice accretion. The output of the prediction model is gray-scale image of ice shape. Ice shape data set was generated through numerical simulation method based on NACA0012 airfoil. To ensure the credibility of the generated data, wind tunnel test data was used to verify the numerical simulation method. Prediction model was established with five input parameters:liquid water content, median volumetric diameter, freezing time, temperature and flight speed, and was then trained and validated in simulation. The simulation results show that the proposed method can predict the ice shape quickly, and the main features of the predicted ice shape such as outline, upper and lower limits, position of ice horn and thickness fit well with the results of numerical simulation.