As for network security situation prediction, an Improved Genetic Algorithm and Particle Swarm Optimization (IGAPSO) is proposed to optimize Extreme Learning Machine (ELM) neural network, so as to obtain higher prediction precision and faster convergence rate.Firstly, the inertia weight and the learning factor in GAPSO are improved to realize self-adaptation at different stages of execution by defining the dynamic exponential function.Secondly, as for the fixed crossover rate and mutation rate in GAPSO, an adaptive crossover and mutation strategy is proposed.Finally, the IGAPSO is used to optimize the initial weights and deviations of ELM.IGAPSO not only ensures the diversity of the population, but also improves the convergence rate of the algorithm.The simulation results show that the fitting degree of IGAPSO-ELM for network security situation prediction can reach 0.99, and the convergence rate is greatly improved compared with that of the contrast algorithms.