Aiming at the low accuracy and efficiency of traditional methods for network security situation prediction, this paper presents research on network security situation prediction based on the genetic optimization neural network algorithm. The genetic algorithm is employed to optimize the probabilistic neural network in which the fitness function guides the search process to avoid premature convergence to local optima and improve the model’s prediction accuracy. A network security situational awareness model is built, network security situation features are extracted, network security situation is predicted through the genetic optimization neural network algorithm, and accurate prediction results are obtained. The experimental results show that the convergence of the genetic optimization neural network algorithm is better. The accuracy of the network security situation prediction obtained by using the proposed algorithm is generally above 0.95. When the number of sampling points is 900, the prediction time is 2 s, which has a good application performance.