Based on grey GM(1,1) model and BP artificial neural network, the grey-neural network joint model is established, which is used to predict the displacement and wind induced coefficients for large cooling towers. Using the joint model, the influence caused by little raw data is overcome. Furthermore the self-adaptability and predicting precision for wind-induced responses of large cooling towers are enhanced. Through comparative analysis of the wind-induced responses of domestic large hyperbolic cooling tower in aero-elastic model wind tunnel test, it can be found that the prediction results of wind-induced responses and wind vibration coefficients are in good agreement with the experimental results, which shows good validity and stability of the model, and then input parameters of refined research on wind induced response are predicted. The proposed method provides a new and effective idea for the refined research on wind effects of large cooling towers.