[目的/意义]突发事件网络舆情群体极化形成和演化受到来自物理空间、社会空间、信息空间的诸多风险 致因因素影响，群体极化风险识别是在对其演化机理全面梳理基础上，对是否产生风险的判断，是社会风险评估 和管控的前导性环节。[方法/过程]文章论证了极端观点是群体极化风险存在的最主要具象形态，分析了极端观 点的特征，构建了极端观点的TCMCR识别模型，基于自我归类理论中引用的原型性概念来识别极端观点，以元对 比率的值表达观点的原型性，并以突发新冠疫情下的“辉瑞新冠小分子药物”舆情为例进行了实证研究。[结果/结 论]文章构建的模型对突发事件网络舆情群体极化的极端观点进行有效识别，实证研究结果显示观点O13发生了 群体极化现象，形成了群体极化风险。

[Purpose/significance] The formation and evolution of group polarization of network public opinion in emer? gencies are affected by many risk factors from physical space, social space and information space. The identification of group polarization risk is based on the comprehensive sorting and analysis of the formation and evolution mechanism of group polarization risk of network public opinion in emergencies and the judgment of whether the risk is generated. It is the leading link of social risk assessment and control. [Method/process] This paper demonstrates that extreme views are the most important concrete form of group polarization risk, analyzes the characteristics of extreme views, constructs a TCMCR identification model of extreme views. Based on the archetypal concept in self classification theory, extreme views are identified, and the archetypal views are expressed by the value of meta contrast ratio. Taking the public opin? ion of "Pfizer Paxlovid" under the outbreak of COVID-19 as an example, the empirical research was carried out. [Result/conclusion] The model constructed in this paper can effectively identify the extreme viewpoints of group polar? ization of network public opinion in emergencies. The empirical research results show that the phenomenon of group po? larization has occurred in viewpoint O13, which forms the risk of group polarization.