Constructing and applying a multi-turn psychological support dialogue corpus based on the Helping Skills...

With the increasing prominence of mental health issues, automated psychological support dialogue systems have gradually gained attention. However, existing Chinese corpora mostly remain at the level of single-turn Q&A or lack psychological counseling theoretical grounding, making it difficult to cover the progressive interactions common in psychological counseling. Meanwhile, collecting and releasing large-scale real multi-turn dialogues faces challenges related to privacy protection and high costs. To address this, this paper proposes the Helping Skills Chain-of-Thought (HCoT) method, which integrates Helping Skills Theory with Chain-of-Thought prompting. We utilized GPT-4o to rewrite CD-CN single-turn data into a Chinese multi-turn psychological support corpus, HCoT-Corpus. This corpus contains 22,341 dialogues and 211,473 strategy annotations, achieving a systematic expansion in scale, structural depth, and theoretical grounding. Analysis results indicate that HCoT-Corpus demonstrates high structural coherence and multi-strategy collaborative characteristics under the "Exploration-Comfort-Action" three-stage framework. Experimental evaluations show that, compared to baselines like SMILE, the HCoT method achieves the most balanced performance in emotional resonance, strategy application, and structural integrity. Furthermore, HCoT-Chat, fine-tuned on Qwen2.5-7B-Instruct, achieved significant advantages in both automatic metrics and cross-model evaluations. This study demonstrates the HCoT method as a promising path for constructing large-scale, theoretically grounded psychological support dialogue datasets.