Psychological Anxiety Risk Analysis Model Based on Large Language Model Interaction

In recent years, psychological anxiety has emerged as a pervasive mental health issue impacting socioeconomic development and individual well-being, making scientific assessment of anxiety crucial. While traditional manual evaluation methods and machine learning-based automated assessment approaches each possess unique advantages, they often struggle to simultaneously address efficiency, reliability, and feature generalization capabilities, lacking systematic comparative validation with standard clinical tools. To tackle this challenge, this study first proposes an anxiety risk assessment model based on interactive large language models (LLMs). The model is fine-tuned using multi-round psychological counseling dialogue datasets to provide high-quality psychological support. Subsequently, we introduce a dual-channel BERT framework for anxiety risk assessment, comprising a main channel and a high-risk channel. The framework strictly maps symptom dimensions and score ranges from the GAD-7 and HAMA clinical scales. This design enables accurate anxiety risk assessment while promoting psychological intervention through interactive dialogue. Experimental results demonstrate that the proposed model achieves high accuracy (88.86%) and robustness in general case evaluations, maintains 88.20% accuracy in cross-dataset validation, and exhibits superior performance in identifying high-risk cases (99.34% high-risk recall rate), significantly outperforming advanced baseline models like MentalBERT.