Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work. Early screening and identification of at-risk LBCs is crucial, yet challenging due to the severe shortage of mental health professionals, especially in rural areas. While the House-Tree-Person (HTP) test shows higher child participation rates, its requirement for expert interpretation limits its application in resource-scarce regions. To address this challenge, we propose PsyDraw, a multi-agent system based on Multimodal Large Language Models that assists mental health professionals in analyzing HTP drawings. The system employs specialized agents for feature extraction and psychological interpretation, operating in two stages: comprehensive feature analysis and professional report generation. Evaluation of HTP drawings from 290 primary school students reveals that 71.03% of the analyzes achieved High Consistency with professional evaluations, 26.21% Moderate Consistency and only 2.41% Low Consistency. The system identified 31.03% of cases requiring professional attention, demonstrating its effectiveness as a preliminary screening tool. Currently deployed in pilot schools, \method shows promise in supporting mental health professionals, particularly in resource-limited areas, while maintaining high professional standards in psychological assessment.

翻译：留守儿童在中国数量超过6600万，因父母外出务工而面临严重的心理健康挑战。对高风险留守儿童进行早期筛查和识别至关重要，但由于心理健康专业人员严重短缺（尤其是在农村地区），这项工作面临巨大困难。虽然房树人绘画测试显示出更高的儿童参与率，但其对专家解读的要求限制了其在资源匮乏地区的应用。为应对这一挑战，我们提出了PsyDraw——一个基于多模态大语言模型的多智能体系统，旨在协助心理健康专业人员分析房树人绘画。该系统采用专门设计的智能体进行特征提取和心理解读，分两个阶段运行：全面特征分析和专业报告生成。对290名小学生房树人绘画的评估显示，71.03%的分析结果与专业评估达到高度一致性，26.21%达到中度一致性，仅2.41%为低度一致性。该系统识别出31.03%需要专业关注的案例，证明了其作为初步筛查工具的有效性。目前该系统已在试点学校部署，\method展现出在支持心理健康专业人员方面的潜力——特别是在资源有限地区，同时保持了心理评估领域的高专业标准。