Exploring the Panorama of Anxiety Levels: A Multi-Scenario Study Based on Human-Centric Anxiety Level Detection and Personalized Guidance

Faculty of Computer Science and Information Technology, University of Malaya, Malaysia Abstract More and more people are under p ressure from work, life and education. Under these pressures, people will develop an anxious state of mind, or even the initial symptoms of suicide. With the advancement of artificial intelligence technology,large language modeling is currently one of the hottest technologies. It is often used for detecting psychological disorders, however, the current study only gives the categorization result, but does not give an interpretable description of what led to this categorization result. Based on all these imma ture studies, this study adopts a person - centered perspective and focuses on GPT - generated multi - scenario simulated conversations. These simulated conversations were selected as data samples for the study. Various transformer - based encoder models were util ized in the study in order to integrate a classification model capable of identifying different anxiety levels. In addition, a knowledge base focusing on anxiety was constructed in this study using Langchain and GPT4. When analyzing the classification resu lts, this knowledge base was able to provide explanations and reasons that were most relevant to the interlocutor's anxiety situation. The study shows that the developed model achieves more than 94% accuracy in categorical prediction and that the advice pr ovided is highly personalized. Mental health is defined as a state of well - being on the mental, emotional, and social levels [8, 16, 34]. Abnormal anxiety is a very important factor that leads to mental health [3, 19, 43].