为了真实有效地提取网络安全态势要素信息，提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化态势要素获取框架中，根据Agent节点功能的不同，划分为不同的层次。利用主成分分析(Principal Component Analysis, PCA)对训练样本属性进行约简并对特殊属性编码融合处理，按照处理结果改进概率神经网络(Probabilistic Neural Network, PNN)结构，以降低系统复杂度。然后以改进的PNN作为基分类器，结合自适应增强算法，通过基分类器反复迭代、样本权重更新，最后加权融合处理形成最终的强多分类器。实验结果表明，本文模型较文中其他几种方法具有较高的获取准确率和良好的泛化能力。(Firstly, in order to extract the information of network security situation accurately and effectively, a hierarchical frame feature acquisition method based on enhanced probabilistic neural network is proposed. According to different functions of Agent node, the hierarchical feature acquisition framework is divided into different levels. The principal component analysis (PCA) is used to reduce the training sample attributes and the special attribute encoding fusion. The result can be used to optimize the structure of the probabilistic neural network (PNN) so as to reduce the system complexity. Then, the improved PNN is used as the base classifier. Combined with the adaptive enhancement algorithm, the final strong classifier is formed through repeated iteration, weight replacement and weighted fusion. The experimental results show that the proposed model achieve higher accuracy and better generalization ability than other methods.)