With the development of network communication technology and the popularity of social media tools, earthquake-related information has been easily published and disseminated in social networks. This study focuses on obtaining this information and providing guidance for earthquake emergency work. A processing model is proposed to obtain earthquake information from social networks. First, a configuration-driven data acquisition module is designed to acquire earthquake information. Second, according to the characteristics of earthquake information in social media, a seismic emergency thesaurus is selected, and weight is calculated. To solve the low accuracy of inter-class classification, an improved mutual term frequency–inverse document frequency (MTF–IDF) algorithm is proposed. Finally, the thesaurus database is used to classify the acquired earthquake information. By taking the Lushan and Jiuzhaigou earthquakes as examples, the improved MTF–IDF algorithm shows a better effect on the selection of seismic keywords than the traditional TF–IDF algorithm; the F1-measure in classification has increased from 79.86 to 86.93%. The proposed model can rapidly and easily acquire and classify earthquake information according to different sources, which can provide timely information and support for disaster relief.

随着网络通信技术的发展和社交媒体工具的普及，与地震有关的信息已经很容易在社交网络中发布和传播。这项研究的重点是获得这些信息并为地震应急工作提供指导。提出了一种从社交网络获取地震信息的处理模型。首先，配置驱动的数据获取模块被设计为获取地震信息。其次，根据社交媒体中地震信息的特点，选择地震应急词库，并计算权重。为了解决类别间分类的准确性低的问题，提出了一种改进的互项频率-文档逆频率（MTF-IDF）算法。最后，同义词库数据库用于对获取的地震信息进行分类。以芦山和九寨沟地震为例，改进后的MTF-IDF算法对地震关键词的选择效果优于传统的TF-IDF算法。的分类中的F 1量度从79.86％增加到86.93％。所提出的模型可以根据不同的来源快速方便地获取和分类地震信息，从而为灾害提供及时的信息和支持。