With the popularity of social networks, crisis information has brought huge harm to social stability and people’s lives. The identification of crisis information in social networks plays a crucial role in avoiding the occurrence of crisis events and reducing the harm caused by crisis information. Thus, this paper proposes a crisis information recognition model based on Back Propagation neural network to identify crisis information in social networks. First, we analyze the crisis information and find that the content characteristics, user characteristics and propagation characteristics have a great impact on the identification of crisis information. Thus, we extract the key characteristics of crisis information. Secondly, we label the crisis information. Combined with the extracted crisis information characteristics, we construct a feature tag library for crisis information identification. Then, we use BP neural network to train the feature tag library to obtain the evaluation mechanism of crisis information. When analyzing the suspected crisis information, we use the evaluation mechanism to identify the crisis information. Finally, we evaluate the performance of our recognition model. The experimental results show that the proposed model can effectively identify 97.5\(\%\) of crisis information.

随着社交网络的普及，危机信息给社会稳定和人们的生活带来了巨大的危害。社交网络中的危机信息识别对于避免危机事件的发生、减少危机信息造成的危害具有至关重要的作用。因此，本文提出了一种基于反向传播神经网络的危机信息识别模型来识别社交网络中的危机信息。首先，我们对危机信息进行分析，发现内容特征、用户特征和传播特征对危机信息的识别有很大影响。因此，我们提取了危机信息的关键特征。其次，我们对危机信息进行标注。结合提取的危机信息特征，我们构建了一个用于危机信息识别的特征标签库。然后，我们使用BP神经网络训练特征标签库，得到危机信息的评价机制。在分析疑似危机信息时，我们使用评估机制来识别危机信息。最后，我们评估我们的识别模型的性能。实验结果表明，该模型能有效识别 97.5\(\%\)的危机信息。