[目的/意义]公众受到突发公共事件威胁时，极易催生公众对政府机构的信任危机，加剧舆情风险。情感 是推动舆情发展的潜在动因，但目前对公众的情感风险预警却尚未有深入的研究，且未有详细的定义与判断。 [方法/过程]本文以风险沟通理论为基础，从定量角度对公众情感风险的预警程度进行评估，再设计情感风险特征 体系，构建情感风险识别与预警框架，从多角度分析模型的预警性能以及不同的特征对模型的贡献程度。[结果/ 结论]实验结果发现，逻辑回归在情感风险识别任务上表现优秀；TextRCNN和BiLSTM+Attention的组合模型性能 最优，可以高效完成情感风险的识别与预警任务；通过特征消融实验发现情感特征和语义特征均对模型性能的影 响显著；影响力特征贡献度较低；文本向量特征可以提升深度学习模型的风险感知性能。

[Purpose/significance] When the public is threatened by sudden public events, it is easy to create a crisis of trust in government agencies and exacerbate public opinion risks. Emotions are a potential driving force behind the development of public opinion, but there has been little research on the public's emotional risk warning, and there is no detailed definition or judgment. [Method/process] Based on the theory of risk communication, this article evaluates the degree of public emotional risk warning from a qualitative perspective, designs an emotional risk feature system from a quantitative perspective, constructs an emotional risk identification and warning framework, and analyzes the warning performance of the model and the degree of contribution of different features from multiple angles. [Result/con? clusion] The experimental results found that Logistic Regression performed well in emotional risk identification tasks; the combination of TextRCNN and BiLSTM + Attention had the best performance and could efficiently complete the identification and warning tasks for emotional risk; feature ablation experiments found that both emotional and seman? tic features had a significant impact on model performance, while influence features had a low contribution rate; text vector features could improve the risk perception performance of deep learning models.