RAG-TPC: Retrieval Augmented Generation for Teenager Psychological Counseling Using DeepSeek

Abstract

The rising prevalence of adolescent mental health issues underscores the limitations of traditional counselling services in terms of scalability, timeliness, and accessibility. This paper presents RAG-TPC, a Retrieval-Augmented Generation framework built upon the DeepSeek language model for teenage psychological counselling. The system incorporates intent classification, semantic retrieval, and structured prompt-based generation to produce safe, empathetic, and contextually appropriate responses. We construct a domainspecific dataset spanning general distress, mental illness, and SOS emergencies, and employ LoRA-based fine-tuning to enhance intent recognition. Experimental results show that RAG-TPC consistently outperforms competitive LLMs in both classification and response quality. Evaluations by psychological professionals further validate the system’s practical effectiveness and ethical reliability, highlighting its potential for scalable AI-assisted mental health support.