In order to grasp the icing growth characteristics of stay cables in the ice prone area in winter, the grey correlation analysis method is used to analyze the correlation effects of inclination angle, temperature, humidity, wind speed, rainfall and air pressure on the icing thickness of stay cables, and the correlation of each influencing factor is determined, and the weak correlation factors are eliminated. Then, the genetic algorithm (GA) and the whale algorithm (WOA) are combined to select the optimal smoothing factor, and a WOA-GA Optimized Generalized Regression Neural Network (GRNN) method is proposed to predict the icing thickness of stay cables. Its characteristic is to take the mean square deviation of output value and actual value as fitness function, calculate the fitness value of each particle; introduce the crossover and mutation operator of GA algorithm into WOA algorithm, at the same time, with the help of weight update strategy, improve the ability of global optimization, avoid WOA algorithm falling into local optimal solution; finally, through iterative optimization, output the smooth factor corresponding to the minimum fitness value, build GRNN pre optimization model Measurement model. The results show that the correlation of ambient temperature is the highest, followed by dip angle, precipitation, wind speed and relative humidity, and the correlation of atmospheric pressure is the lowest, showing weak correlation; compared with the traditional GRNN, WOA-GRNN and PSO-GA-GRNN models, the GRNN icing prediction model optimized by whale algorithm and genetic algorithm has high accuracy, with the average mean absolute percentage error of 3.58% and root mean square error of 0.58mm ;The sensitivity analysis method is used to evaluate the influence of the influence factors on the accuracy of the model. It is found that the temperature has the greatest influence on the model, followed by the cable inclination.