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Article(id=1315372240078754000, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1315372180473496558, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1736697600000, receivedDateStr=2025-01-13, revisedDate=1741795200000, revisedDateStr=2025-03-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1791538780468, onlineDateStr=2026-10-09, pubDate=1771084800000, pubDateStr=2026-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791538780468, onlineIssueDateStr=2026-10-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791538780468, creator=13701087609, updateTime=1791538780468, updator=13701087609, issue=Issue{id=1315372180473496558, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='2', pageStart='225', pageEnd='448', issueExtLink='null', onlineDate='null', pubDate='1771084800000', pubDateStr='2026-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791538766258, creator='13701087609', updateTime=1791538833264, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1315372461596725883, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1315372180473496558, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1315372461596725884, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1315372180473496558, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=225, endPage=234, ext={EN=ArticleExt(id=1315372240263303377, articleId=1315372240078754000, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=Research on the mechanism and practical path of context driven innovation in Large Language Model industry architecture, columnId=1309226307205034609, journalTitle=Studies in Science of Science, columnName=Hot Issues, runingTitle=null, highlight=null, articleAbstract=

The large language model is highland of global technological competition and engine for economic development. As new innovation paradigm emerging in the digital economy era, exploring the mechanism and path of context driven innovation in the industrial architecture of large language model has important theoretical value and practical significance for solving major application and industrialization problems and better supporting high-quality economic development. Based on the theory of context driven innovation, exploring the connotation, core elements, and process mechanisms of context driven innovation in the industrial architecture of large language model industries. It summarizes the specific practical path of context driven innovation in the industrial architecture of large language industries, and verifies and concretizes the practical path through multiple case analyses. It has been found that under the context driven innovation paradigm, the mechanism of innovation in large language model industry architecture can be summarized into four key links: major mission and strategic traction, scenario identification and classification, industrial architecture innovation process, and innovation effects; The innovation of large language model industry architecture under the context driven innovation paradigm covers the systematic integration of technological architecture, the dual transformation of organizational architecture, and the integration of industrial chain architecture. The research results provide important theoretical basis and practical guidance for promoting the higher-level application of large language model and better supporting the high-quality development of the digital economy.

, authors=Shao-shuai LI1, 2, Ling CHEN1, 2, authorsList=Shao-shuai LI, Ling CHEN, authorCompany=null, correspAuthors=Ling CHEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1315372240917614806, articleId=1315372240078754000, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=场景驱动大模型产业架构创新的机理及路径研究, columnId=1309226307364418163, journalTitle=科学学研究, columnName=热点议题, runingTitle=null, highlight=null, articleAbstract=

数字经济时代大模型是全球科技竞争新高地和经济发展新引擎,而场景驱动作为数字经济时代涌现的全新创新范式,探索场景驱动大模型产业架构创新的机理与路径对解决大模型应用和产业化问题,更好支撑经济高质量发展具有重要的理论价值和实践意义。基于场景驱动创新理论,探讨场景驱动大模型产业架构创新的内涵特征、核心要素以及作用机理,探究场景驱动大模型产业架构创新的实践路径,并通过多案例分析对实践路径进行验证与具象化阐述。研究发现:场景驱动大模型产业架构的作用机理包括战略与使命牵引、场景识别与分类、产业架构创新过程以及创新效应4个关键环节;场景驱动创新范式下的大模型产业架构创新涵盖技术架构的系统性整合、组织架构的双重转型以及产业链架构的贯通融合。研究结果为促进大模型更高水平应用,更好支撑数字经济高质量发展提供了重要的理论依据与实践指导。

, authors=李少帅1, 2, 陈玲1, 2, authorsList=李少帅, 陈玲, authorCompany=null, correspAuthors=陈玲, authorNote=

李少帅(1989-),男,助理研究员,博士。

, correspAuthorsNote=
陈玲(1977-),女,教授、博士生导师, E-mail: 。
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李少帅(1989-),男,助理研究员,博士。

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Bulletin of Chinese Academy of Sciences, 2021, 36(12):1404-1412., articleTitle=Promoting organized basic research: Strategic layout and strategic capacity in science and technology, refAbstract=null)], funds=[Fund(id=1315372243887182081, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, awardId=2023ZD0121700, language=CN, fundingSource=科技创新2030—“新一代人工智能”重大项目(2023ZD0121700), fundOrder=null, country=null), Fund(id=1315372243983651074, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, awardId=GZC20240796, language=CN, fundingSource=国家资助博士后研究人员计划(GZC20240796), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1315372241307685079, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, xref=1, ext=[AuthorCompanyExt(id=1315372241320267992, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, companyId=1315372241307685079, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 School of Public Policy & Management, Tsinghua University, Beijing 100084, China), AuthorCompanyExt(id=1315372241332850905, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, companyId=1315372241307685079, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 清华大学公共管理学院, 北京 100084)]), AuthorCompany(id=1315372241395765466, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, xref=2, ext=[AuthorCompanyExt(id=1315372241408348379, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, companyId=1315372241395765466, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 Center for Industrial Development and Environmental Governance, Tsinghua University, Beijing 100084, China), AuthorCompanyExt(id=1315372241416736988, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, companyId=1315372241395765466, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 清华大学产业发展与环境治理研究中心, 北京 100084)])], figs=[ArticleFig(id=1315372242708582643, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=EN, label=Figure 1, caption=Mechanism of context driven innovation in industrial architecture of large language model, figureFileSmall=vJrGjmPBSv0hxBhVK9XFFQ==, figureFileBig=EMXWvxcdnhESikHFKuR04A==, tableContent=null), ArticleFig(id=1315372242779885812, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=CN, label=图1, caption=场景驱动大模型产业架构创新的作用机理, figureFileSmall=vJrGjmPBSv0hxBhVK9XFFQ==, figureFileBig=EMXWvxcdnhESikHFKuR04A==, tableContent=null), ArticleFig(id=1315372242968629493, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=EN, label=Figure 2, caption=The practical path of context driven innovation in large language model industry architecture, figureFileSmall=ilmftxNwuExL2pBeC9QBvA==, figureFileBig=dzMqa9CRXzf44ID6w0C1JA==, tableContent=null), ArticleFig(id=1315372243048321270, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=CN, label=图2, caption=场景驱动大模型产业架构创新的实践路径, figureFileSmall=ilmftxNwuExL2pBeC9QBvA==, figureFileBig=dzMqa9CRXzf44ID6w0C1JA==, tableContent=null), ArticleFig(id=1315372243140595959, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=EN, label=Table 1, caption=

Comparison between context driven innovation and other types of innovation

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创新范式 创新内涵 创新特质 创新过程
场景驱动创新 以场景为载体,多种创新要素的有机协同整合与多元化应用 可量化的数据构成,数字技术支持和数字化流程 技术研发与应用同步进行的共时性创新
需求驱动创新 基于市场或用户的具体需求来推动技术和产品的开发 依赖对市场需求的信息掌握和理解 从需求反馈挖掘到技术创新应用的线性过程
技术驱动创新 通过技术革新满足发展需求,并形成相应的商业模式与策略 注重技术的前沿性和突破性,以及可能性应用 从技术研发、突破到成果转化并推向市场的线性过程
数据驱动创新 通过海量数据挖掘与分析,以数据为基础进行决策和创新 科学性与准确性,助力精细化运营与管理 从数据收集到最终数据驱动决策的多阶段循环过程
), ArticleFig(id=1315372243207704824, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=CN, label=表1, caption=

场景驱动创新与其他创新类型的比较

, figureFileSmall=null, figureFileBig=null, tableContent=
创新范式 创新内涵 创新特质 创新过程
场景驱动创新 以场景为载体,多种创新要素的有机协同整合与多元化应用 可量化的数据构成,数字技术支持和数字化流程 技术研发与应用同步进行的共时性创新
需求驱动创新 基于市场或用户的具体需求来推动技术和产品的开发 依赖对市场需求的信息掌握和理解 从需求反馈挖掘到技术创新应用的线性过程
技术驱动创新 通过技术革新满足发展需求,并形成相应的商业模式与策略 注重技术的前沿性和突破性,以及可能性应用 从技术研发、突破到成果转化并推向市场的线性过程
数据驱动创新 通过海量数据挖掘与分析,以数据为基础进行决策和创新 科学性与准确性,助力精细化运营与管理 从数据收集到最终数据驱动决策的多阶段循环过程
), ArticleFig(id=1315372243270619385, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=EN, label=Table 2, caption=

Summary of data acquisition methods and data encoding

, figureFileSmall=null, figureFileBig=null, tableContent=
数据来源 数据获取方式 编码
实地调研访谈 现场访谈 对案例对象进行调研访谈,与相关负责人、领导等深度交流。 I-1
参与体验 分别使用不同案例对象的大模型产品、服务等,并撰写使用体验报告 I-2
案例公开数据 官方网站 官网发布的与本研究相关的布局、技术、产品服务等信息共计46条 O-1
社交平台 案例对象在主要社交平台发布的与本研究相关的信息共计54条 O-2
新闻以及期刊 新闻报道 主流媒体对案例对象大模型相关的信息报道共计31篇 J-1
学术期刊 CNKI和WOS期刊数据库中与本研究相关的文献共计49篇 J-2
权威研究报告 研究院所 案例对象研究院所、实验室等公开发布的相关研究报告3篇 R-1
咨询公司 专业管理咨询公司和智库机构发布的大模型相关研究报告16篇 R-2
), ArticleFig(id=1315372243341922554, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=CN, label=表2, caption=

数据获取方式及数据编码汇总

, figureFileSmall=null, figureFileBig=null, tableContent=
数据来源 数据获取方式 编码
实地调研访谈 现场访谈 对案例对象进行调研访谈,与相关负责人、领导等深度交流。 I-1
参与体验 分别使用不同案例对象的大模型产品、服务等,并撰写使用体验报告 I-2
案例公开数据 官方网站 官网发布的与本研究相关的布局、技术、产品服务等信息共计46条 O-1
社交平台 案例对象在主要社交平台发布的与本研究相关的信息共计54条 O-2
新闻以及期刊 新闻报道 主流媒体对案例对象大模型相关的信息报道共计31篇 J-1
学术期刊 CNKI和WOS期刊数据库中与本研究相关的文献共计49篇 J-2
权威研究报告 研究院所 案例对象研究院所、实验室等公开发布的相关研究报告3篇 R-1
咨询公司 专业管理咨询公司和智库机构发布的大模型相关研究报告16篇 R-2
), ArticleFig(id=1315372243421614331, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=EN, label=Table 3, caption=

Evidence of innovation in context driven large language model technology architecture

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创新过程 核心构念 典型事实证据援引
技术创新场景需求 数据融合与智能化;算法性能优化;算力拓展提升;多模态能力升级 GPT、星火等需要增强多模态能力并在文本、视觉和音频理解方面表现更加出色(J-2,R-1、2);数据融合、算法性能及算力拓展成为星火大模型增强任务智能化水平的关键(J-2)
核心要素协同整合 创新要素升级;软件、硬件、服务战略协同 GPT-4o在硬件、软件及服务设定新标杆,实现多方的协同提升(O-1,R-1、2);星火大模型通过数据融合与增强,推出“飞星一号”算力平台等措施,推动软硬件以及服务协同(J-2)
技术架构创新方式 人类和机器反馈的强化学习;微调与迁移学习等;应用场景深度定制;系统级技术架构创新 GPT基于人类和机器反馈的强化学习、引入微调与迁移学习机制等推动技术架构调整(J-1,O-2);星火大模型的迭代已从单一的模型优化转变为场景极其细分、深度定制优化、工程持续补充、持续效果运营的系统工程(J-1、2)
技术创新效应 模型泛化与适应能力增强;生产力提质增效;模型更加安全稳定 GPT通过技术创新增强了泛化能力和适应性,并使模型更加安全和稳定(I-2;J-2);GPT在各行业应用提升生产力并实现降本增效(O-2,R-2);星火大模型极大地提升了教育场景、材料科学等场景的泛化性能和提质增效潜力(I-1、2;O-1、2)
), ArticleFig(id=1315372243509694716, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=CN, label=表3, caption=

场景驱动大模型技术架构创新的事实证据

, figureFileSmall=null, figureFileBig=null, tableContent=
创新过程 核心构念 典型事实证据援引
技术创新场景需求 数据融合与智能化;算法性能优化;算力拓展提升;多模态能力升级 GPT、星火等需要增强多模态能力并在文本、视觉和音频理解方面表现更加出色(J-2,R-1、2);数据融合、算法性能及算力拓展成为星火大模型增强任务智能化水平的关键(J-2)
核心要素协同整合 创新要素升级;软件、硬件、服务战略协同 GPT-4o在硬件、软件及服务设定新标杆,实现多方的协同提升(O-1,R-1、2);星火大模型通过数据融合与增强,推出“飞星一号”算力平台等措施,推动软硬件以及服务协同(J-2)
技术架构创新方式 人类和机器反馈的强化学习;微调与迁移学习等;应用场景深度定制;系统级技术架构创新 GPT基于人类和机器反馈的强化学习、引入微调与迁移学习机制等推动技术架构调整(J-1,O-2);星火大模型的迭代已从单一的模型优化转变为场景极其细分、深度定制优化、工程持续补充、持续效果运营的系统工程(J-1、2)
技术创新效应 模型泛化与适应能力增强;生产力提质增效;模型更加安全稳定 GPT通过技术创新增强了泛化能力和适应性,并使模型更加安全和稳定(I-2;J-2);GPT在各行业应用提升生产力并实现降本增效(O-2,R-2);星火大模型极大地提升了教育场景、材料科学等场景的泛化性能和提质增效潜力(I-1、2;O-1、2)
), ArticleFig(id=1315372243580997885, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=EN, label=Table 4, caption=

Evidence of context driven innovation in organizational structure of large language model

, figureFileSmall=null, figureFileBig=null, tableContent=
创新过程 核心构念 典型事实证据援引
组织创新场景需求 面向AI产业化发展;面向国家发展战略与使命 行业对提效降成、改善用户体验及创新业务模式等需求和大模型核心能力推动其面向产业化(J-1、R-2);面向国家对大模型标准制定、社会经济发展、风险治理等需求(J-1、R-2)
核心要素协同整合 组织结构社群化;组织协作高能化 阿里发布魔塔社区及通义星辰、通义点金等应用模型,组织结构社群化特征显现(I-2;O-2);中国科学院自动化研究所作为紫东太初大模型主要研发单位,基于国产化软硬件基础,与华为、阿里、京东等企业合作,走出自主创新之路(O-2)
组织架构创新方式 建设跨部门协同机制;打造融合开放的组织体系;实施有组织的科研应用 阿里通过跨部门协作整合多方力量,促进大模型研发与应用(J-1);中国科学院自动化研究所联合合作伙伴共同打造融合开放的组织体系,通过明确定位、组建专业团队等,实施有组织科研(O-1)
组织创新效应 组织运行效率提升;产业发展新动能;提高服务国家发展能力 通义大模型提供不同任务复杂度下的低成本高效率选择,满足企业多样化需求,助力运行效率提升和新动能发展(I-1、2;O-1);紫东太初大模型走出了自主创新之路,为服务中国式现代化建设注入新的活力(O-2)
), ArticleFig(id=1315372243648106750, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=CN, label=表4, caption=

场景驱动大模型组织架构创新的事实证据

, figureFileSmall=null, figureFileBig=null, tableContent=
创新过程 核心构念 典型事实证据援引
组织创新场景需求 面向AI产业化发展;面向国家发展战略与使命 行业对提效降成、改善用户体验及创新业务模式等需求和大模型核心能力推动其面向产业化(J-1、R-2);面向国家对大模型标准制定、社会经济发展、风险治理等需求(J-1、R-2)
核心要素协同整合 组织结构社群化;组织协作高能化 阿里发布魔塔社区及通义星辰、通义点金等应用模型,组织结构社群化特征显现(I-2;O-2);中国科学院自动化研究所作为紫东太初大模型主要研发单位,基于国产化软硬件基础,与华为、阿里、京东等企业合作,走出自主创新之路(O-2)
组织架构创新方式 建设跨部门协同机制;打造融合开放的组织体系;实施有组织的科研应用 阿里通过跨部门协作整合多方力量,促进大模型研发与应用(J-1);中国科学院自动化研究所联合合作伙伴共同打造融合开放的组织体系,通过明确定位、组建专业团队等,实施有组织科研(O-1)
组织创新效应 组织运行效率提升;产业发展新动能;提高服务国家发展能力 通义大模型提供不同任务复杂度下的低成本高效率选择,满足企业多样化需求,助力运行效率提升和新动能发展(I-1、2;O-1);紫东太初大模型走出了自主创新之路,为服务中国式现代化建设注入新的活力(O-2)
), ArticleFig(id=1315372243719409919, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=EN, label=Table 5, caption=

Evidence of context driven innovation in the industrial chain architecture of large language model

, figureFileSmall=null, figureFileBig=null, tableContent=
创新过程 核心构念 典型事实证据援引
产业应用场景需求 模型硬件研发;模型训练;模型计算;模型应用适配 产业链对模型硬件性能、训练与计算效率等需求更具体迫切(O-1);混元大模型基于MoE架构提升训练与推理效率,应用适配能力强大(O-1);百度自研芯片昆仑提供算力保障、飞浆支持分布式训练和推理部署(O-2,J-1)
核心要素协同整合 促进产业链贯通;产业链与创新链融合 混元大模型适配特定场景需求,并结合垂直领域数据完成领域专属模型落地(O-1);文心一言大模型构建芯片层、框架层、模型层以及应用层的全栈布局,(O-2,R-2);混元大模型与生态伙伴开展产品及应用场景的共创和研学,促进产业链与创新链之间的融合(O-1)
产业链架构创新方式 构建核心平台;打造产业创新生态;跨区域协同创新 腾讯提供TI、HAI等开放平台,使模型在开发、部署和应用链条上无缝衔接,通过整合优质内容生态,打造多样化的产业创新生态(O-1);混元大模型通过与全球开源社区合作,促进技术扩散和跨区域协作(J-2);文心一言支持分布式动态加载,适用于跨机环境(O-2)
产业链创新效应 赋能现有产业链升级;加速形成新兴产业业态 在金融、教育、医疗等行业推动智能化转型,加速现有产业智能化升级(O-1,J-2);推动元宇宙内容创作等新兴产业业态形成与发展(I-2;R-2)
), ArticleFig(id=1315372243790713088, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372240078754000, language=CN, label=表5, caption=

场景驱动大模型产业链架构创新的事实证据

, figureFileSmall=null, figureFileBig=null, tableContent=
创新过程 核心构念 典型事实证据援引
产业应用场景需求 模型硬件研发;模型训练;模型计算;模型应用适配 产业链对模型硬件性能、训练与计算效率等需求更具体迫切(O-1);混元大模型基于MoE架构提升训练与推理效率,应用适配能力强大(O-1);百度自研芯片昆仑提供算力保障、飞浆支持分布式训练和推理部署(O-2,J-1)
核心要素协同整合 促进产业链贯通;产业链与创新链融合 混元大模型适配特定场景需求,并结合垂直领域数据完成领域专属模型落地(O-1);文心一言大模型构建芯片层、框架层、模型层以及应用层的全栈布局,(O-2,R-2);混元大模型与生态伙伴开展产品及应用场景的共创和研学,促进产业链与创新链之间的融合(O-1)
产业链架构创新方式 构建核心平台;打造产业创新生态;跨区域协同创新 腾讯提供TI、HAI等开放平台,使模型在开发、部署和应用链条上无缝衔接,通过整合优质内容生态,打造多样化的产业创新生态(O-1);混元大模型通过与全球开源社区合作,促进技术扩散和跨区域协作(J-2);文心一言支持分布式动态加载,适用于跨机环境(O-2)
产业链创新效应 赋能现有产业链升级;加速形成新兴产业业态 在金融、教育、医疗等行业推动智能化转型,加速现有产业智能化升级(O-1,J-2);推动元宇宙内容创作等新兴产业业态形成与发展(I-2;R-2)
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场景驱动大模型产业架构创新的机理及路径研究
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李少帅 1, 2 , 陈玲 1, 2
科学学研究 | 热点议题 2026,44(2): 225-234
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科学学研究 |热点议题 2026 , 44 (2) : 225 -234
场景驱动大模型产业架构创新的机理及路径研究
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李少帅(1989-),男,助理研究员,博士。

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李少帅1, 2, 陈玲1, 2
作者信息
  • 1 清华大学公共管理学院, 北京 100084
  • 2 清华大学产业发展与环境治理研究中心, 北京 100084
通讯作者:
陈玲(1977-),女,教授、博士生导师, E-mail: 。
作者简介:

李少帅(1989-),男,助理研究员,博士。

Research on the mechanism and practical path of context driven innovation in Large Language Model industry architecture
Shao-shuai LI1, 2, Ling CHEN1, 2
Affiliations
  • 1 School of Public Policy & Management, Tsinghua University, Beijing 100084, China
  • 2 Center for Industrial Development and Environmental Governance, Tsinghua University, Beijing 100084, China
出版时间: 2026-02-15
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摘要
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数字经济时代大模型是全球科技竞争新高地和经济发展新引擎,而场景驱动作为数字经济时代涌现的全新创新范式,探索场景驱动大模型产业架构创新的机理与路径对解决大模型应用和产业化问题,更好支撑经济高质量发展具有重要的理论价值和实践意义。基于场景驱动创新理论,探讨场景驱动大模型产业架构创新的内涵特征、核心要素以及作用机理,探究场景驱动大模型产业架构创新的实践路径,并通过多案例分析对实践路径进行验证与具象化阐述。研究发现:场景驱动大模型产业架构的作用机理包括战略与使命牵引、场景识别与分类、产业架构创新过程以及创新效应4个关键环节;场景驱动创新范式下的大模型产业架构创新涵盖技术架构的系统性整合、组织架构的双重转型以及产业链架构的贯通融合。研究结果为促进大模型更高水平应用,更好支撑数字经济高质量发展提供了重要的理论依据与实践指导。

场景驱动  /  大模型产业架构  /  作用机理  /  实践路径  /  多案例研究

The large language model is highland of global technological competition and engine for economic development. As new innovation paradigm emerging in the digital economy era, exploring the mechanism and path of context driven innovation in the industrial architecture of large language model has important theoretical value and practical significance for solving major application and industrialization problems and better supporting high-quality economic development. Based on the theory of context driven innovation, exploring the connotation, core elements, and process mechanisms of context driven innovation in the industrial architecture of large language model industries. It summarizes the specific practical path of context driven innovation in the industrial architecture of large language industries, and verifies and concretizes the practical path through multiple case analyses. It has been found that under the context driven innovation paradigm, the mechanism of innovation in large language model industry architecture can be summarized into four key links: major mission and strategic traction, scenario identification and classification, industrial architecture innovation process, and innovation effects; The innovation of large language model industry architecture under the context driven innovation paradigm covers the systematic integration of technological architecture, the dual transformation of organizational architecture, and the integration of industrial chain architecture. The research results provide important theoretical basis and practical guidance for promoting the higher-level application of large language model and better supporting the high-quality development of the digital economy.

context driven  /  large language model industry architecture  /  mechanism  /  practical path  /  multiple case studies
李少帅, 陈玲. 场景驱动大模型产业架构创新的机理及路径研究. 科学学研究, 2026 , 44 (2) : 225 -234 .
Shao-shuai LI, Ling CHEN. Research on the mechanism and practical path of context driven innovation in Large Language Model industry architecture[J]. Studies in Science of Science, 2026 , 44 (2) : 225 -234 .
正文
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大模型正处于从上游核心技术布局向下游产业化应用拓展,由基础大模型向行业大模型发展延伸的关键时期[1]。世界各国纷纷将发展大模型作为获取国际竞争主导权的战略阵地,我国围绕构建现代产业体系和新质生产力发展要求系统性布局。习近平总书记在第二十次全国代表大会上强调要构建新一代信息技术、人工智能等一批新的增长引擎。推动现代服务业同先进制造业、现代农业深度融合,加快现代产业体系建设。2023年中央经济工作会议进一步指出“要大力推进新型工业化,发展数字经济,加快推动人工智能发展”。北京、上海、深圳、杭州、成都等出台措施加速大模型研发和应用落地,如《北京市推动“人工智能+”行动计划(2024-2025年)》指出,到2025年底,通过实施多个标杆型应用工程和示范性应用项目,力争形成一批先进可用、自主可控的基础大模型产品和优秀的行业大模型产品。
大模型创新过程中的场景驱动特征和作用逐步凸显。其一,传感器与物联网发展实现了多模态数据广泛采集与运用,先前模糊的场景特征能被具象化和可视化呈现。其二,场景与大模型定制化与泛化能力合理适配,特定场景为大模型定制化开发提供情景化支撑,而跨领域场景为大模型泛化能力形成与发展提供了广阔空间。我国大模型发展面临算力需求激增、权威数据和公共数据开发程度低、顶尖人才培养滞后、模型复用能力差等现实困境[2]。学界聚焦人机交互、信息管理、应用生态及风险治理等议题[3-5],业界试图通过提升技术先进性、加码算力设施等激发大模型发展动能,但效果却差强人意。通过场景驱动大模型发展才是广阔的“新蓝海”。
鉴于此,本研究将探索场景驱动大模型产业架构创新的作用机理并探讨其实践路径。第一,场景驱动大模型产业架构创新的内涵及特征是什么?第二,场景驱动大模型产业架构创新的核心要素有哪些,作用机理如何?第三,场景驱动大模型产业架构创新的实践路径如何?本研究主要创新之处在于揭示场景驱动大模型产业架构的作用机理,并归纳场景驱动大模型技术、组织、产业链架构创新的差异化实践路径,这对深入理解与应用场景驱动创新范式、加快大模型产业发展具有重要理论与实践价值。
1 理论基础与文献梳理
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1.1 场景驱动创新
“场景”(context)原为电影学概念,指特定时间、空间及人物关系构成的具象画面。后引入管理学领域并将其定义为个体生活中的特定情境及在这种情境下产生的需求或情感因素[6],或个体在场景中发挥主观能动性并且引发环境或自身变革的过程[7,8]。国外学者更多将场景驱动创新理解为企业拟解决用户特定痛点,将现有产品或者服务应用于特定环境,将创新要素有机融合,实现技术变革和市场更替的过程[9]。国内学者则将场景驱动创新解释为以场景为载体,以战略或使命为引领,驱动技术、市场等创新要素有机协同整合与多元化应用[10]。
场景驱动创新与需求、技术驱动创新等既区别又联系(见表1)。与需求驱动创新相比,场景驱动创新虽然与其都关注需求对创新的推动效应,但场景驱动创新包含需求驱动创新中的用户需求并将其上升为共性的、长远的、清晰的发展目标;与技术驱动创新相比,场景驱动创新浓缩在真实的市场验证环境中,从先前的技术研发到转化的历时性创新,迈向技术研发与应用同步进行的共时性创新;与数据驱动创新相比,场景驱动创新更加注重实际应用,通过满足特定场景下的需求来创造价值,数据驱动创新则侧重通过对海量数据挖掘与分析,优化现有工作流程和探寻新的商业机会。此外,场景驱动创新范式下的创新主导者从研究机构、高等院校等走向科技创新企业和创业者,参与主体形成了来自科学界、产业界等各方的创新联合体。
近年来场景驱动创新在健康医疗、制造业及农业等领域的研究与应用渐起。武建龙等[11]揭示了生物医疗场景驱动企业颠覆性创新的作用机制;康健等[12]提出了中国制造企业应依据自身在国内和全球双重价值链网络的嵌入广度和高度设计合理的场景驱动创新方式;唐欣和许永斌[13]探讨了场景驱动农业全产业链创新的要素体系与理论逻辑,归纳场景驱动农业全产业链创新的实践路径。但是现有关于场景驱动研究的领域、过程及结论等都存在较大差异,整体呈分散化特征,也鲜有从场景驱动视角讨论大模型产业架构创新发展的理论框架,难以为大模型产业架构创新提供基于场景视角的思路参考。
1.2 大模型及其产业架构
大模型通常指具有大规模参数和复杂计算结构的机器学习模型,通常由深度神经网络构建而成,拥有数十亿甚至数千亿级别的参数,其有望成为数字经济的基础设施和创新平台,形成以大模型为核心的产业生态。学者们从多方面对大模型产业架构开展探讨。(1)关注技术架构对大模型的底层支撑。多模态大模型由于蕴含更加丰富的场景、带来更好用户体验而受到关注[14],同时分布式训练正成为大模型技术架构的重要发展方向[15];(2)重视组织架构对大模型的价值创造。施锦诚和王迎春[1]分析了大模型产业生态,认为其形成离不开大模型提供者、参与者、部署者、使用者、促进者差异化的功能发挥;(3)注重产业链架构对大模型的落地应用。作为大模型产业生态的基本特征,架构者影响整个产业生态链构建与演进[16,17],大模型产业发展将推动形成包括上游基础硬件层、中游研发训练层、运营管理层以及下游场景应用层内在的新产业链[1],同时大模型产业链架构正在从单一产业链迈向多元化、从封闭产业链迈向开放产业链、从线性产业链迈向循环产业链[18]。
2 场景驱动大模型产业架构创新的作用机理
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2.1 场景驱动大模型产业架构创新内涵及特征
本文认为场景驱动大模型产业架构创新是以相关战略与使命为牵引,以人工智能技术创造性应用为导向、供需联动为路径,通过场景开发开放驱动大模型与产业深度融合,实现技术迭代升级和产业快速增长的过程。场景驱动大模型产业架构创新具有如下特征:
(1)战略导向性。即面向国家关键性需求、明确场景驱动攻坚难题,建设全新的价值主张和解决方案。如场景驱动大模型瞄准经济发展、社会民生、城市治理等战略需求,通过精细化任务设计和技术体系建构,加速技术革新进程、破解科技成果转化低效等困境;(2)创新引领性。即以前瞻性构想和开拓性实践为起点,运用新模式、新方法推动大模型应用场景落地。不再局限于市场需求挖掘和技术应用示范,而通过关键技术迭代、系统平台优化升级等革新技术创新模式与价值创造方式;(3)开放融合性。即推动各类创新主体开放场景机会,围绕场景创新加快资本、人才、技术、数据等要素汇聚,促进创新链与产业链深度融合,提供更多智能化解决方案,提升整体生产力水平;(4)配置精细性。即场景驱动使创新资源精准投入关键领域,提高资源利用效率。数字技术与数据要素使特定场景下的问题和痛点识别更精确,促使场景匹配和场景驱动多元主体创新更加精准高效,提高创新应用效率与质量[19]。
2.2 场景驱动大模型产业架构创新的核心要素
场景驱动大模型产业架构创新需要创新要素合理配置、创新主体相互配合以及创新底座支撑。(1)创新要素。即数据、算法和算力,通过大量多样化的数据输入,大模型才能学习和模拟人类语言模式、逻辑推理等复杂能力,而高效的算法能提升模型训练效率和准确性,还能降低开发门槛、增强模型泛化应用能力,强大的算力资源能处理和训练海量数据集、支撑大模型训练与优化;(2)创新主体。包括以数字平台为代表的模型提供者,以中小企业与开发者为代表的模型部署者,以数据提供商、算力提供商等生态合作伙伴为代表的模型参与者,以政府机构、高等院校以及科技型中介机构为代表的模型促进者,以运用大模型进行生产生活的组织与个体为代表的模型使用者;(3)创新底座。主要指以芯片、服务器、计算平台与框架等构成的创新基础设施,不仅支撑大模型产业链和创新链条内外融合贯通,而且与创新要素和主体要素相互影响与赋能,共同推动大模型产业架构的持续性创新。
2.3 场景驱动大模型产业架构创新的作用机理
对大模型而言,场景驱动创新的作用过程强调以战略与使命牵引、场景识别与分类、产业架构创新过程及创新效应等内容。
首先,战略与使命牵引。党中央、国务院及国家部委等从国家战略层面出发,充分挖掘和发挥我国场景优势,围绕人工智能应用和产业化问题决策部署,形成场景驱动大模型产业架构创新的战略共识。各参与方共同开发新的市场和需求,实现价值共创和成果共享新局面;其次,场景识别与分类。驱动大模型产业架构创新的场景可概括为技术创新场景、组织创新场景以及产业应用场景三种类型,这些场景不仅影响大模型产业生态形成与发展,而且为大模型赋能社会经济发展与社会进步提供了方向性指引;再次,产业架构创新过程。以技术创新、组织创新以及产业应用场景的需求为导向,围绕需求痛点与问题难点对核心要素进行协同整合,重新形成新的价值共创生态体系[11],进而实现多元化场景价值;最后,创新效应。场景驱动大模型技术架构创新、组织架构创新以及产业链架构创新,实现AI高水平应用与赋能经济高质量发展并且两种创新效应相辅相成[20]。此外,创新效应会逆向激发场景创新需求,推动原有场景开放和新场景建设,从而形成场景驱动大模型产业架构创新的飞轮效应[21]。本研究归纳场景驱动大模型产业架构创新的作用机理如图1所示。
3 场景驱动大模型产业架构创新路径的案例探讨
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3.1 研究方法与案例选取
场景驱动大模型产业架构创新的实践路径取决于具体场景需求和问题提炼,同时需要指出的是特定场景需求与相应实践路径并非严苛对应而与其他路径完全断离,但与其他路径的因果关系通常较弱且难以从学理上得到充分解释,故为更加清晰地阐述场景驱动大模型产业架构创新的具体过程,本研究从技术创新、组织创新以及产业应用场景的突出问题入手,采用多案例研究场景驱动大模型技术架构、组织架构以及产业链架构的典型创新路径。
案例选择遵循理论抽样原则,多案例研究的最佳数量为3-6个[22],同时基于案例选择的典型性、目的抽样性、数据可获得性及研究便利性[23],并结合实际调研、搜索引擎检索等渠道得到的实际反馈情况,选取GPT、星火、通义等6家大模型为案例对象,具体理由如下:(1)案例对象综合实力强大并在大模型行业领域影响广泛,是大模型创新发展的重要推动者;(2)案例对象围绕不同场景需求进行了诸多实践并积累了宝贵经验,与本研究性质与目的较为契合;(3)案例对象数据较为全面,能获取数据的渠道较为多样化,便于不同来源数据的相互比较和印证。
3.2 数据收集及编码
本研究采用多元化的数据收集渠道进行案例数据搜集,除对部分案例对象调研访谈外,包括通过案例对象的官方网站、主流媒体报道、社交平台、学术论文、研究报告等,同时在对多个案例对象数据资料搜集过程中进行交叉检验,以提高案例研究的信度与效度[24,25]。此外,为降低主观认知性偏差,以概念操作化为依据,关注场景驱动大模型产业架构创新过程中的核心构念。数据获取方式及数据编码见表2。
3.3 案例分析及发现
(1)场景驱动大模型技术架构创新的实践路径
大模型技术架构创新属于系统性工程,技术创新场景为技术架构创新提供全过程的载体支撑。场景驱动大模型技术架构系统性整合创新的实践路径如下:
首先,技术创新场景需求解析。大模型技术架构创新涉及创新要素更迭及多模态能力升级,根据大模型实际技术创新需求,可将其场景需求概分为数据融合与智能化、算力拓展提升、算法性能优化及多模态能力升级(见表3);其次,核心要素协同整合。技术创新场景不仅催动创新要素升级,而且驱动硬件、软件、服务领域的战略协同[26]。星火大模型通过数据融合增强和多模态能力实现从数据驱动到数据智能驱动的升级。为应对技术创新带来的算力需求激增,GPT利用分布式计算和专用集成电路等提升算力水平,星火大模型则与华为合作推出“飞星一号”算力平台,使推理效率提升25%。GPT和星火大模型主要借助训练策略改进、高级优化器等实现算法性能提升。在上述创新要素升级基础上,进一步从系统层面实现硬件、软件以及服务的整合优化,提升模型性能与运行效率;再次,技术架构创新实现方式。GPT系列模型和星火大模型都采用Transformer神经网络结构,但技术架构创新方式不同,GPT通过引入基于人类和机器的反馈强化学习、微调与迁移学习机制等增强其作为通用AI平台的潜力,以应对具体场景需求和风险挑战。星火大模型技术架构创新已从模型优化转变为场景深度定制,以满足各类应用场景的开发需求,同时具备对特定应用场景的微调能力,这种系统级的创新使星火大模型在实际应用中展现更强性能与潜力。
最后,技术创新效应。基于场景驱动的大模型技术架构创新实现了适应性与泛化性提升、生产效率与质量提高、安全与隐私增强。此外,技术创新效应实现又逆向推动场景驱动创新能力持续完善,并在新一轮场景驱动技术创新中通过飞轮效应使技术架构创新在场景价值实现方面展现更大潜能与更广泛应用前景。
(2)场景驱动大模型组织架构创新的实践路径
人工智能产业化应用和国家发展战略与使命两种场景共同驱动大模型组织架构的双重转型。场景驱动大模型组织架构双重转型的实践路径如下:
首先,组织创新场景需求解析。根据场景需求作用领域和对象不同,大模型组织创新的场景需求可概分为面向人工智能产业化应用和国家发展战略与使命,如通义大模型为许多行业发展提供需要的商业化能力,紫东太初大模型以点带面推动我国产业技术革新,走出了自主创新之路(见表4);其次,核心要素协同整合。产业化应用场景驱动下,通义大模型不同类型创新主体向医疗、教育等领域聚集,形成高度聚集的社群形态以促进商业价值共创共享。国家发展战略与使命场景驱动下,紫东太初大模型超越之前以深度产学研体系为基础、核心技术突破与市场化应用的一般创新联合体,而以实现国家科技创新和数字经济高质量发展为目标的高能级创新联合体;再次,组织架构创新实现方式。一方面通过建设跨部门协作机制实现组织结构的社群化转型,如阿里通过跨部门协作整合云计算、人工智能及物联网等多部门力量推进模型研发,并形成了围绕具体应用场景的社群化结构。另一方面通过打造融合开放的组织体系和实施有组织的科研与应用,实现组织协作的高能化转型,如中国科学院自动化研究所面向国家科技创新与高质量发展,打造大模型融合开放的组织体系。通过联合不同创新主体实施有组织的科研与应用,形成了政产学研用一体化的协同创新机制,实现战略导向与市场需求的有机结合[27]。
最后,组织创新效应。场景驱动大模型组织架构的双重转型不仅实现了运行效率的大幅提升,激活大模型产业发展新动能、推动产业创新与发展,而且提高了服务国家发展战略的能力。此外,组织创新效应带来了更多现实需求并推动下一轮场景驱动大模型组织架构创新过程,借助创新飞轮效应大模型组织架构在持续创新中愈发合理高效。
(3)场景驱动大模型产业链架构创新的实践路径
大模型产业链创新涉及上游基础硬件层、中游研发训练层及下游场景应用层的全面创新,通过对核心要素进行重组,各环节形成供需联动的路径,形成有效的产业链贯通融合机制。场景驱动大模型产业链架构贯通融合创新的实践路径如下:
首先,产业应用场景需求解析。产业应用场景涵盖产业链的不同环节,主要包括大模型硬件研发、大模型训练、大模型计算、应用适配场景(见表5);其次,核心要素协同整合。场景驱动促进大模型产业链上下游产业主体借助数字设施共享、数据要素互通等加强各环节联系,使产业链保持顺畅贯通,如混元大模型已被腾讯300多项内部业务介入并联合生态伙伴提供超过50个行业解决方案,展示了从技术研发到商业化应用的完整链条,文心一言构建了芯片层、框架层、模型层和应用层的全栈布局,打通了训练与部署环节的衔接。同时,场景驱动给予模型研发需要的真实数据与产业资源,加速产业链与创新链深度融合;再次,产业链架构创新初步形成了以核心平台、产业创新生态以及跨区域协同创新为主的宝贵经验。如混元大模型通过TI、HAI等开放平台提供从数据输入、模型训练到模型管理的一站式服务,文心一言通过智能体平台贯通产业链各环节并加速从要素整合到价值实现的创新链条全过程。混元和文心一言还通过多模态能力和广泛的行业覆盖,形成普适性的创新生态体系,通过跨区域协同创新模式使基于场景驱动的大模型产业链上下游结合更加顺畅高效。
最后,产业链创新效应。一方面有效赋能现有产业实现转型升级。从提高生产效率到增强决策支持,再到促进产品和服务创新以及提升客户服务水平,场景驱动的大模型都在推动诸多行业的革新与发展。另一方面催生全新商业模式和服务,加速形成新物种、新兴产业业态。此外,产业链创新效应实现又带动了新的产业应用场景需求,新一轮的场景驱动大模型产业链架构创新过程将更加广泛和深刻。
上述对场景驱动大模型技术架构、组织架构及产业链架构创新过程的案例探讨,其共同之处在于GPT、星火、通义等案例验证了“场景需求解析→核心要素协同整合→架构创新实现方式→创新效应”的实践路径,不同之处在于技术创新场景、组织创新场景及产业应用场景在实践路径的具体环节上均存在显著差异。场景驱动大模型产业架构创新的实践路径如图2所示。
4 研究结论与启示
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4.1 研究结论及贡献
本文基于场景驱动创新理论,探索场景驱动大模型产业架构创新的作用机理及实践路径,研究结论如下:
第一,场景驱动大模型产业架构创新是以相关战略与使命为牵引,以人工智能技术的创造性应用为导向、供需联动为路径,通过场景驱动大模型技术与产业深度融合,从而实现技术迭代升级和产业快速增长的过程,其特征可归纳为战略导向性、创新引领性、开放融合性、配置精细性。场景驱动大模型产业架构创新涉及技术架构、组织架构以及产业链架构创新;第二,场景驱动大模型产业架构创新涉及创新要素(数据、算法、算力)、创新主体(模型提供者、部署者等)以及创新底座(芯片、服务器等)。场景驱动大模型产业架构创新的作用机理蕴含全新的整合观与系统观,其过程可凝练为“战略与使命牵引→场景识别与分类→产业架构创新过程→创新效应”,并可借助“创新飞轮”效应实现新一轮的场景驱动创新;第三,场景驱动大模型产业架构创新形成了不同的实践路径,包括对技术架构的系统性整合、组织架构的双重转型以及产业链架构的贯通融合,进一步通过GPT、星火等多案例分析为上述实践路径提供来自事实层面的证据支持。
本文理论贡献在于:(1)将场景驱动大模型产业架构创新的类型概括为技术创新、组织创新及产业应用,为技术架构创新、组织架构创新以及产业链架构创新提供了场景依据;(2)揭示了场景驱动大模型产业架构创新的作用机理,为大模型产业架构创新过程研究提供了理论参考;(3)通过多案例研究探讨了场景驱动大模型产业架构创新的具体实践路径,响应学界关于场景驱动的创新需要聚焦社会新型议题的呼吁[10],贡献于场景驱动的新兴数字产业创新研究。
4.2 实践启示及展望
本文实践启示在于:(1)场景驱动成为大模型技术升级和产业增长的新方向。一方面着力打造适合大模型的应用场景,鼓励在大模型渗透率较高的制造、影视、教育等行业深入挖掘应用场景,另一方面加快推动面向大模型的场景开放,鼓励各类主体建立大模型场景开放清单,建设与完善场景开放清单的征集、遴选以及发布机制;(2)加强建设场景驱动和多方协同的大模型良性竞合生态,注重场景驱动大模型创新“飞轮效应”的功能发挥,做大行业蛋糕同时关注细分行业的创新发展机会,创造让“百模大战”寻找差异化竞争优势的空间,进一步推动其形成国家层面的大模型生态竞争优势。
场景驱动创新范式与大模型产业创新发展属于新兴议题,本研究不足与未来展望主要有:(1)伴随人工智能应用场景的加速涌现与变化,如逐渐从当前的业务类场景向决策管理场景深入,以及其他新兴技术在大模型应用程度持续加深,这都要求场景驱动创新范式具备及时自我革新和迁移的强大动态能力,以更好适应最新变化;(2)尝试探讨场景驱动其他行业创新发展的理论逻辑与具体过程,并梳理形成最佳实践、典型案例库等,继而将场景驱动创新的宝贵经验应用到更多领域;(3)可尝试构建场景驱动的创新绩效评估体系,推动场景驱动创新由理论探讨向量化研究转变,更加精准地测度场景驱动带来的创新效应。
基金
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  • 科技创新2030—“新一代人工智能”重大项目(2023ZD0121700)
  • 国家资助博士后研究人员计划(GZC20240796)
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  • 接收时间：2025-01-13
  • 首发时间：2026-10-09
  • 出版时间：2026-02-15
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  • 收稿日期：2025-01-13
  • 修回日期：2025-03-13
基金
科技创新2030—“新一代人工智能”重大项目(2023ZD0121700)
国家资助博士后研究人员计划(GZC20240796)
作者信息
    1 清华大学公共管理学院, 北京 100084
    2 清华大学产业发展与环境治理研究中心, 北京 100084

通讯作者:

陈玲(1977-),女,教授、博士生导师, E-mail: 。
参考文献
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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