This study explores the use of ChatGPT in depression intervention through explainable deep learning. The research aims to assess the feasibility of ChatGPT as a tool for counselors to interact with patients and compare its effectiveness with human-generated content (HGC). A novel framework integrating ChatGPT, BERT, and SHAP is proposed to enhance the accuracy and effectiveness of mental health interventions. ChatGPT generates responses to user inquiries, which are then classified using BERT to ensure content reliability. SHAP is used to provide insights into the underlying semantic constructs of AI-generated recommendations, enhancing interpretability. The results show that the proposed methodology achieved an impressive accuracy rate of 93.76%. ChatGPT consistently uses a polite and considerate tone, avoiding complex vocabulary and maintaining an impersonal demeanor. These findings highlight the potential of AI-generated content (AIGC) as a valuable complement to traditional intervention strategies. The study discusses the significant promise of large language models in healthcare, representing a key step toward developing advanced healthcare systems that can improve patient care and counseling practices. The research also highlights the importance of interpretability in AI systems, emphasizing the need for explainable artificial intelligence (XAI) techniques such as SHAP to enhance the transparency and reliability of AI-generated recommendations. The study compares AIGC and HGC, identifying distinguishing linguistic attributes and contributing to a deeper understanding of the linguistic properties of AI-generated recommendations. The results demonstrate that AIGC can perform better classification while generating interpretable, symptom-based explanations. The study concludes that the integration of advanced AI technologies, including BERT and Roberta, enhances depression intervention measures by generating personalized and context-aware recommendations. The proposed framework prioritizes interpretability and transparency, providing valuable insights into the underlying reasoning behind AI-generated recommendations. The study also emphasizes the importance of linguistic analysis in understanding the characteristics of AIGC and HGC, contributing to the validity and reliability of AI-generated recommendations. The findings suggest that ChatGPT can effectively support decision-making in chat interactions, accurately predicting patients' depression measurement tools and clinical assessment behaviors.This study explores the use of ChatGPT in depression intervention through explainable deep learning. The research aims to assess the feasibility of ChatGPT as a tool for counselors to interact with patients and compare its effectiveness with human-generated content (HGC). A novel framework integrating ChatGPT, BERT, and SHAP is proposed to enhance the accuracy and effectiveness of mental health interventions. ChatGPT generates responses to user inquiries, which are then classified using BERT to ensure content reliability. SHAP is used to provide insights into the underlying semantic constructs of AI-generated recommendations, enhancing interpretability. The results show that the proposed methodology achieved an impressive accuracy rate of 93.76%. ChatGPT consistently uses a polite and considerate tone, avoiding complex vocabulary and maintaining an impersonal demeanor. These findings highlight the potential of AI-generated content (AIGC) as a valuable complement to traditional intervention strategies. The study discusses the significant promise of large language models in healthcare, representing a key step toward developing advanced healthcare systems that can improve patient care and counseling practices. The research also highlights the importance of interpretability in AI systems, emphasizing the need for explainable artificial intelligence (XAI) techniques such as SHAP to enhance the transparency and reliability of AI-generated recommendations. The study compares AIGC and HGC, identifying distinguishing linguistic attributes and contributing to a deeper understanding of the linguistic properties of AI-generated recommendations. The results demonstrate that AIGC can perform better classification while generating interpretable, symptom-based explanations. The study concludes that the integration of advanced AI technologies, including BERT and Roberta, enhances depression intervention measures by generating personalized and context-aware recommendations. The proposed framework prioritizes interpretability and transparency, providing valuable insights into the underlying reasoning behind AI-generated recommendations. The study also emphasizes the importance of linguistic analysis in understanding the characteristics of AIGC and HGC, contributing to the validity and reliability of AI-generated recommendations. The findings suggest that ChatGPT can effectively support decision-making in chat interactions, accurately predicting patients' depression measurement tools and clinical assessment behaviors.