Abstract: The anxiety detection task involves the utilization of natural language processing techniques to analyze users?? content posted on social media. To promote research on anxiety detection among college students, a Weibo dataset has been developed. Addressing the high cost of data annotation, this paper adopts an unsupervised approach to analyze zero-shot data under the context of public health emergencies, and proposes an adaptive model called domain adaptive network based on contrastive learning (DAN-CL). DAN-CL, based on a pre-trained language model, integrates source domain text and target domain text into a knowledge distillation-based adversarial network for domain adaptive training, enabling knowledge transfer of the model. It also employs contrastive learning methods to enhance the generalization ability of the model. Benchmark experimental results show that DAN-CL outperforms existing comparative models, and ablation experiments further validate the effectiveness of different components within the model. Additionally, an analysis of the psychological status of college students reveals that changes in their anxiety levels align with the trends of sudden public health events. Under the influence of disease progression and the surrounding environment, their anxiety levels increase significantly. A specific analysis of the causes of anxiety and the countermeasures provides theoretical support for addressing college students?? mental health challenges during and after sudden public health events.

Key words: anxiety psychology, domain adaptive, contrastive learning, sentiment analysis

摘要： 焦虑心理检测任务是利用自然语言处理技术对用户在社交媒体上发布的内容进行分析。为了促进对高校学生焦虑心理检测的研究，开发了一个微博数据集。针对数据集标注成本高等问题，从无监督的角度对突发公共卫生事件背景下的零样本数据进行分析，提出了基于领域对比学习的自适应模型（DAN-CL）。DAN-CL基于预训练语言模型，将源域文本和目标域文本输入到结合知识蒸馏的对抗网络中进行领域自适应训练，实现模型的知识迁移，并采用对比学习方法提高模型的泛化能力。基准实验结果表明，DAN-CL的性能优于现存的对比模型，消融实验进一步证明了模型中不同部分的有效性。对高校学生的心理状况进行分析，发现其心理焦虑变化情况与突发性公共卫生事件变化的趋势是一致的，受疾病发展与周围环境的影响其焦虑水平显著升高。对焦虑产生的原因进行具体分析并提出对策，为突发性公共卫生事件下以及后时代应对大学生的心理健康挑战提供理论支持。

关键词: 焦虑心理, 领域自适应, 对比学习, 情感分析