目的 为提高群智设计中发包方对设计需求的精确识别与描述能力，通过对大量在线评论进行分析，挖掘市场需求，并利用大语言模型生成更准确、全面的设计需求。方法 爬取电商平台中的在线评论文本并识别、分析在线评论中的评价主体，然后通过谱聚类对评价主体进行归类，计算用户满意度，并通过大语言模型对话完成需求生成。结果 该方法能系统地分析在线评论中的用户反馈，识别并聚类评价主体，进而理解和生成用户需求。实验证明该方法还能有效地生成众包设计需求。 结论 该研究有效地整合了在线评论文本需求挖掘，以及通过大模型来识别和生成设计需求，实现了对设计需求的精确定位和高效生成，为群智设计提供了一种新的实施途径。

The work aims to analyze a large number of online reviews to uncover market requirements and use big language models to generate more accurate and comprehensive design requirements, so as to improve the ability of the contract-issuing party in crowdsourced design and accurately identify and describe design requirements. By scraping online review texts from e-commerce platforms, the system identified and analyzed the subjects of the reviews, categorized them using spectral clustering, calculated user satisfaction, and generated requirements through large language model dialogues. This method systematically analyzed user feedback from online reviews, identified and clustered the subjects of the reviews, and then identified and generated user requirements. Experiments demonstrated that this method effectively generated requirements of crowdsourced design. This research successfully integrates online review-based requirement mining and large language models to identify and generate design requirements, achieving precise targeting and efficient generation of design requirements and providing a new approach for crowdsourced design.