The work aims to predict and simulate the viscosity of edible ink by studying the genetic algorithm to optimize the Back Propagation (BP) neural network to establish the relationship between the independent variable and the dependent variable. Based on the previous researches on edible inks, the acetic acid concentration, chitosan dosage, alcohol dosage, and grinding speed were used as independent variables, and the obtained viscosity of edible ink as the dependent variable. The experiment was first designed by orthogonal experiments, and then the BP neural network with genetic algorithm was used to predict and simulate the viscosity of edible ink. 30 sets of experimental data were obtained by orthogonal experimental design: by using Genetic Algorithm Optimization Toolbox (GAOT) in Matlab 2018a software, a neural network with a convergence accuracy of 104 was obtained after 38 iterative trainings. The relative error between the predicted value of viscosity and the corresponding true value of viscosity was between 0.05% and 3.7%, and the R2 value of the fit was 0.8672, indicating that the BP neural network had excellent predictive ability and high predictive accuracy to predict the viscosity of edible inks. The BP neural network optimized with genetic algorithm can be used to predict and simulate the viscosity of edible inks, and extend the neural network into the evaluation system of other performances of the edible ink, thereby providing guidance for the production and application of the edible ink.