The work aims to improve the prediction ability of BP neural network for droplet spreading behavior during electrojet printing. A whale optimization algorithm (WOA) was proposed to optimize the droplet spreading prediction model based on BP neural network. Firstly, the numerical model of droplet spreading under the action of electric field was established by the phase field method, and the accuracy of the simulation results was verified by experiments. Then, the initial diameter, impact velocity, contact angle and electric field strength were selected as input parameters for the neural network, the maximum spreading diameter was taken as the output parameter of the neural network, and the initial weights and thresholds in the neural network were optimized by the whale optimization algorithm to construct the droplet spreading prediction model. Finally, the prediction model was trained and tested based on the simulation results, and was compared and analyzed with the traditional BP neural network model. Compared with the traditional BP neural network prediction model, the mean absolute error and root mean square error of the WOA-BP neural network prediction model were reduced by 72.60% and 77.60% respectively, while the mean absolute percentage error was reduced from 15.029 3% to 4.585 3%. It is demonstrated that the WOA-BP neural network prediction model can better predict the droplet spreading and can provide a new method for the prediction of droplet spreading.