Existing Artificial Intelligence （AI） psychological counseling systems are generally limited to the counselor role， making it difficult to address users' dual needs for both professional advice and emotional support dynamically. To overcome this limitation， a psychological health Large Language Model （LLM） based on dual-role interaction mechanism， named STAR （Supportive Therapeutic Adaptive Responder）， was proposed. First， a dynamic role switching mechanism based on system prompts was designed to enable stylistic shifts between the psychological counselor and friend roles， thereby addressing users' dual needs for both professional advice and emotional support in psychological counseling scenarios. Then， a training process incorporating data quality control was implemented， where model feedback was continuously collected to dynamically optimize both data generation strategies and system prompt design， thereby forming a closed loop between data generation and model training and enhancing dataset quality. Experimental results on the Chinese psychology benchmark testing framework CPsyCoun （Chinese Psychological Counseling） show that the STAR model achieves improvements of 9.80% in comprehensive score of comprehensiveness， professionalism， and authenticity over the base model Qwen2.5-7B-Instruct， and 26.81% over the single-role psychological LLM EmoLLM （Emotional Large Language Model）. STAR model also outperforms models fine-tuned on open-source datasets such as SoulChat， as well as general-purpose LLMs like GPT-4o-mini. The STAR model achieves dual-role adaptive responding effectively， significantly enhancing both effect and experience of psychological counseling.