In order to strengthen the construction of network security defense system and effectively respond to new types of threat attacks appearing in the network environment, this paper constructs a network security threat prediction model using data mining algorithms. The network security threat posture needs to be assessed before the security threat prediction. Accordingly, this paper assesses the four security threat postures of services, vulnerabilities, weaknesses, and hosts on the basis of the quantitative assessment method of hierarchical security threat posture. After that, a network security threat prediction model is constructed based on the support vector mechanism, and a genetic algorithm is used to optimize the parameters of the model. The three evaluation index values of MAE, RMSE and MAPE for the GA-SVM-based cybersecurity posture prediction method proposed in this paper are 0.0106, 0.0133 and 0.0222, respectively, which are better than those of the ABC-SVM-based and PSO-SVM-based prediction methods. It indicates that the method in this paper has smaller error and higher accuracy in cyber security posture prediction. This shows that the method in this paper usually achieves better accuracy in cyber security threat posture prediction.