Natural Medicinal Materials (NMMs) have a long history of global clinical applications, accompanied by extensive informational records. Despite their significant impact on healthcare, the field faces a major challenge: the non-standardization of NMM knowledge, stemming from historical complexities and causing limitations in broader applications. To address this, we introduce a Systematic Nomenclature for NMMs, underpinned by ShennongAlpha, an AI-driven platform designed for intelligent knowledge acquisition. This nomenclature system enables precise identification and differentiation of NMMs. ShennongAlpha, cataloging over ten thousand NMMs with standardized bilingual information, enhances knowledge management and application capabilities, thereby overcoming traditional barriers. Furthermore, it pioneers AI-empowered conversational knowledge acquisition and standardized machine translation. These synergistic innovations mark the first major advance in integrating domain-specific NMM knowledge with AI, propelling research and applications across both NMM and AI fields while establishing a groundbreaking precedent in this crucial area.

翻译：天然药物（NMMs）在全球临床应用中历史悠久，积累了丰富的文献信息。尽管其在医疗保健领域具有重要影响，该领域仍面临重大挑战：由于历史复杂性导致的天然药物知识非标准化问题，制约了其更广泛的应用。为解决这一问题，我们提出了一套天然药物系统命名体系，并依托AI驱动平台神农阿尔法（ShennongAlpha）实现智能知识获取。该命名体系能够实现对天然药物的精准识别与区分。神农阿尔法收录了超过一万种天然药物的标准化双语信息，显著提升了知识管理及应用能力，从而克服了传统障碍。此外，该平台率先实现了AI赋能的知识对话获取与标准化机器翻译。这些协同创新标志着天然药物领域知识与AI融合的首次重大突破，不仅推动了天然药物与AI领域的研究与应用，更在这一关键领域开创了突破性先例。