The mean volumetric diameter （MVD） and liquid water content （LWC） are two important parameters that affect aircraft icing， but they are difficult to be measured accurately in practice. If these two parameters can be accurately obtained in real time， they can provide some guidance for icing prediction and the establishment of aircraft airworthiness certification standards. In this paper， a prediction model of icing meteorological parameters based on the genetic-algorithm-optimized neural network is proposed. We use the ice thickness and icing rate of different combinations of measuring points， ambient temperature， flight speed and wing angle of attack as input parameters， and the icing meteorological parameters MVD and LWC as output parameters， and develop a prediction model of icing meteorological parameters optimized by the genetic algorithm， This model predicts the icing meteorological parameters of the numerical calculation test group data and the icing wind tunnel experiment data. The results show that the relative error of the prediction model for numerical calculation of icing meteorological parameters of the test group is within 10%， and the relative error of the experiment data is within 20%. This method is feasible.