To deal with the time-varying and nonlinear properties of network security and its difficulty in prediction assessment, a network security situation prediction method based on Harmony Search algorithm and Relevance Vector Machine (HS-RVM) was proposed to offset the prediction accuracy drawbacks of existing prediction methods. In the prediction process, network security situation samples were firstly normalized and phase space was reconstructed; then, Harmony Search (HS) algorithm was adopted to find out the optimal Relevance Vector Machine (RVM) hyper parameters to build the network security situation prediction model with improved prediction accuracy and velocity; finally, Wilcoxon signed rank tests were used to testify the difference of prediction performance. The simulation cases indicate that the Mean Absolute Percentage Error (MAPE) and the Root-Mean-Square Error (RMSE) of the proposed prediction method are 0.49575 and 0.02096 respectively, with a better prediction performance than the Improved Harmony Search (IHS) algorithm and Regularized Extreme Learning Machine (IHS-RELM) prediction model and PSO and Support Vector machine for Regression (PSO-SVR) prediction model. The outcome of Wilcoxon signed rank tests show there is a significant difference. The proposed method is capable to depict the changing rules of network security situation relatively, which is helpful for network administrators to control the changing tendency of network security situation in time.