企业如何优化AI搜索？GEO白皮书详解生成式引擎优化

摘要

　　This whitepaper systematically elaborates, for the first time, on the definition, core framework, market value, and development path of the emerging field known as Generative Engine Optimization (GEO). Research indicates that as generative AI accounts for over 40% of information acquisition, the traffic distribution logic of traditional Search Engine Optimization (SEO) is undergoing a fundamental reconstruction. GEO helps enterprises establish sustainable cognitive assets and growth momentum in an AI-native environment by building "topical authority," optimizing semantic architecture, and adapting to multimodal retrieval.

本白皮书首次系统阐述了生成式引擎优化（Generative Engine Optimization，简称 GEO）这一新兴领域的定义、核心框架、市场价值与发展路径。研究显示，随着生成式 AI 在信息获取中占比突破 40%，传统搜索引擎优化（SEO）的流量分配逻辑正在发生根本性重构。GEO 通过构建“主题权威GEO的核心思维，要求企业在特定垂直领域构建不可替代的权威性，成为AI系统眼中某个细分问题最可靠、最全面、最及时的信息来源。”、优化语义架构、适配多模态检索，助力企业在 AI 原生环境中建立可持续的认知资产与增长动能。

　　Based on a survey of hundreds of enterprises across China (including 48.6% foreign trade enterprises), in-depth interviews with industry experts, and technical empirical analysis, this report proposes for the first time the "Four-Dimensional Capability Model" for GEO implementation and the "C-ARM Index System" for effectiveness evaluation. The study finds that enterprises that systematically deployed GEO strategies early saw an average increase of 147% in brand mention rate on AI platforms and a 35% reduction in the acquisition cycle for high-quality leads. The report also points out three major challenges currently facing the industry: lack of standards, compliance uncertainty, and a talent gap.

本报告基于对全国数百家企业（含 48.6%的外贸企业）的调研、数十位行业专家的深度访谈，并结合技术实证分析，首次提出 GEO 实施的“四维能力模型本报告首次提出的GEO实施框架，包括语义深度理解与知识图谱构建、AI检索逻辑适配与内容优化、多模态内容生态布局、全域分发与可信溯源四个维度。”与效果评估的“C-ARM 指标体系”。研究发现，早期系统部署 GEO 策略的企业，其 AI 端品牌提及率平均提升 147%，高质量线索获取周期缩短 35%。报告同时指出，当前行业面临标准缺失、合规不确定性及人才断层三大挑战。

　　We advocate that all industry stakeholders should jointly promote the standardization of GEO methodologies, establish cross-border data compliance collaboration mechanisms, and accelerate the cultivation of "AI + Marketing" interdisciplinary talent to seize the trillion-dollar global market restructuring opportunity brought by generative AI.

我们倡议，行业各方应共同推进 GEO 方法论标准化、建立跨境数据合规协作机制，并加速培育“AI+营销”复合型人才，以把握生成式 AI 带来的万亿级全球市场重构机遇。

第一章：引言——当生成式 AI 成为新入口

1.1 研究背景：不可逆的范式迁移

　　By the end of 2024, over 35% of users in major global markets prioritized generative AI tools for information acquisition, with this figure rising to 51% in B2B procurement decision-making scenarios. The traditional search engine logic based on keyword matching and link authority is being supplemented or even replaced by generative engines based on natural language understanding, contextual reasoning, and knowledge sourcing. Enterprise marketing must shift from competing for "rankings on search engine results pages" to building "core trusted sources within AI answers."

截至 2024 年底，全球主要市场有超过 35%的用户将生成式 AI 工具作为优先信息获取方式，在 B2B 采购决策场景中，这一比例高达 51%。传统基于关键词匹配与链接权重的搜索引擎逻辑，正在被基于自然语言理解、上下文推理与知识溯源的生成式引擎所补充甚至替代。企业营销需从争夺“搜索结果页面排名”转向构建“AI 答案内核心信源”。

1.2 核心概念：什么是 GEO？

　　Generative Engine Optimization (GEO) is a comprehensive set of strategic and technical practices designed to optimize the structure, semantics, authority, and multimodal adaptability of an enterprise's digital content. The goal is to increase the probability of this content being recognized, cited, and integrated as a core component of high-quality answers by generative AI systems, thereby achieving sustained brand exposure, precise traffic, and trust endorsement in the new era of AI-driven information distribution.

生成式引擎优化（GEO）A content optimization strategy for generative AI search engines, focusing on making content understandable and recommendable by AI systems.是一整套系统性的战略与技术实践，旨在通过优化企业数字内容的结构、语义、权威性与多模态适配度，使其更大概率被生成式 AI 系统识别、引用并整合为高质量答案的核心组成部分，从而在 AI 驱动的信息分发新时代，获得持续性品牌曝光、精准流量与信任背书。

1.3 GEO 与 SEO 的本质区别

维度	传统 SEO	GEO
优化目标	搜索引擎结果页面（SERP）排名	AI 生成答案中的引用率与答案权重
> Optimization Goal	> Ranking on Search Engine Results Pages (SERP)	> Citation Rate and Answer Weight in AI-generated responses
核心逻辑	关键词密度、反向链接、页面权威	主题权威GEO的核心思维，要求企业在特定垂直领域构建不可替代的权威性，成为AI系统眼中某个细分问题最可靠、最全面、最及时的信息来源。性、语义密度、逻辑链完整度、知识关联
> Core Logic	> Keyword Density, Backlinks, Page Authority	> Topical Authority, Semantic Density, Logical Chain Integrity, Knowledge Association
内容单元	网页（Page）	知识片段（Knowledge Fragment）GEO时代的内容单元，指可以被AI系统识别和引用的结构化知识单元，区别于传统SEO以网页为单位的优化对象。
> Content Unit	> Web Page	> Knowledge Fragment
评估指标	排名位置、自然流量、点击率	AI 引用率、答案渗透率、上下文相关性评分
> Evaluation Metrics	> Ranking Position, Organic Traffic, Click-Through Rate	> AI Citation Rate, Answer Penetration Rate, Contextual Relevance Score
技术焦点	爬虫适配、链接建设	RAG 架构适配、多模态对齐、动态语义库
> Technical Focus	> Crawler Adaptation, Link Building	> RAG Architecture Adaptation, Multimodal Alignment, Dynamic Semantic Library

1.4 研究方法论

　　This study employs a mixed-methods approach combining quantitative and qualitative research.

本研究采用定量与定性相结合的方法：

定量调研： 通过线上问卷，收集 825 家中国企业（覆盖制造、零售、电商、服务贸易等）有效数据。

Quantitative Survey: Collected valid data from 825 Chinese enterprises (covering manufacturing, retail, e-commerce, service trade, etc.) via online questionnaires.
专家访谈： 深度访谈 43 位从业者，包括企业 CMO、AI 平台产品经理、跨境法律专家、学术研究者。

Expert Interviews: Conducted in-depth interviews with 43 practitioners, including enterprise CMOs, AI platform product managers, cross-border legal experts, and academic researchers.
案例研究： 匿名化深入分析 12 个国内及跨境 GEO 实施案例。

Case Studies: Conducted anonymized, in-depth analysis of 12 domestic and cross-border GEO implementation cases.
技术测试： 基于主流 AI 模型进行内容优化对比实验。

Technical Testing: Performed comparative experiments on content optimization using mainstream AI models.

第二章行业现状：曙光初现的蓝海市场

2.1 市场规模与增长预测

　　According to calculations in this research report, the market size for GEO-related technical services and consulting for Chinese enterprises in 2024 was approximately 2.8 billion RMB. It is projected to grow to 13.5 billion RMB by 2027, with a compound annual growth rate (CAGR) of 68%. Among this, the demand for GEO in cross-border expansion scenarios is growing faster than in the domestic market, with its share expected to increase from 45% in 2024 to 60% by 2027.

据本研究报告测算，2024 年中国企业端 GEO 相关技术服务与咨询市场规模约为 28 亿元人民币，预计到 2027 年将增长至 135 亿元，年复合增长率（CAGR）达 68%。其中，跨境出海场景的 GEO 需求增速高于国内市场，预计占比将从 2024 年的 45%提升至 2027 年的 60%。

2.2 企业认知与采纳度调研

认知阶段： 72%的受访企业知晓生成式 AI 对营销的影响，但仅有 19%的企业已开始系统性部署 GEO 策略，处于“认知高、行动早”的窗口期。

Awareness Stage: 72% of surveyed enterprises are aware of the impact of generative AI on marketing, but only 19% have begun systematically deploying GEO strategies, indicating a window of "high awareness, early action."
投入意愿： 年营销预算超 500 万的企业中，有 58%表示将在未来 12 个月内增加 GEO 相关投入。

Investment Willingness: Among enterprises with an annual marketing budget exceeding 5 million RMB, 58% indicated they would increase GEO-related investment within the next 12 months.
核心驱动： 排名前三的驱动因素为：“获取早期技术红利”（65%）、“应对传统流量成本上升”（57%）、“建立品牌在 AI 时代的新认知资产”（52%）。

Core Drivers: The top three driving factors are: "Gaining early technological dividends" (65%), "Coping with rising traditional traffic costs" (57%), and "Establishing new brand cognitive assets in the AI era" (52%).

2.3 产业链生态初显

　　An industry chain is rapidly forming, primarily consisting of:

产业链正在快速形成，主要包括：

技术基础设施层： 大模型平台、向量数据库、语义理解 API 服务商。

Technical Infrastructure Layer: Large model platforms, vector databases, semantic understanding API providers.
GEO 专业服务层： 垂直领域服务商（如本报告联合发布方之一亿企邦科技，专注于跨境与国内整合 GEO）、全案营销机构的新业务部。

GEO Professional Services Layer: Vertical service providers (e.g., Yi Qi Bang Technology, a co-publisher of this report, focusing on integrated cross-border and domestic GEO), new business divisions of full-service marketing agencies.
工具 SaaS 层： AI 内容优化工具、GEO 效果监测平台。

Tool SaaS Layer: AI content optimization tools, GEO effectiveness monitoring platforms.
企业应用层： 从大型品牌到中小外贸工厂的广泛需求方。

Enterprise Application Layer: A wide range of demand-side entities, from large brands to small and medium-sized foreign trade factories.

第三章 GEO 核心方法论与实践框架

3.1 思维跃迁：从“关键词”到“主题权威GEO的核心思维，要求企业在特定垂直领域构建不可替代的权威性，成为AI系统眼中某个细分问题最可靠、最全面、最及时的信息来源。”

　　GEO requires a shift in enterprise marketing mindset from competing for isolated keyword rankings to building irreplaceable "topical authority" within specific vertical domains. This means an enterprise needs to become the most reliable, comprehensive, and timely source of information in the eyes of AI systems for a particular niche question (e.g., "European safety standards for small electric excavators").

GEO 要求企业营销思维从争夺孤立关键词排名，转向在特定垂直领域构建不可替代的“主题权威GEO的核心思维，要求企业在特定垂直领域构建不可替代的权威性，成为AI系统眼中某个细分问题最可靠、最全面、最及时的信息来源。”。这意味着一家企业需要成为 AI 系统眼中，某个细分问题（如“小型电动挖掘机欧洲安全标准”）最可靠、最全面、最及时的信息来源。

3.2 四维实施能力模型

　　Successful GEO deployment relies on the synergy of four core capabilities:

成功的 GEO 部署依赖于四大核心能力的协同：

　　维度一：语义深度理解与知识图谱构建

Dimension 1: Deep Semantic Understanding and Knowledge Graph Construction

实践： 建立动态更新的行业专业语义库，将产品参数、应用场景、解决方案、常见问答等，以“概念-属性-关系-实例”的结构组织。

Practice: Establish a dynamically updated industry-specific semantic library, organizing product parameters, application scenarios, solutions, FAQs, etc., in a "concept-attribute-relationship-instance" structure.
案例价值： 某工业阀门制造商构建了包含 5000 余个专业术语的多语种知识图谱，使其在回答“化工流程中高温阀门选型”相关 AI 问题时，引用率提升 300%。

Case Value: An industrial valve manufacturer built a multilingual knowledge graph containing over 5,000 professional terms, increasing its citation rate by 300% in AI responses related to "high-temperature valve selection in chemical processes."

　　维度二：AI 检索逻辑适配与内容优化

Dimension 2: AI Retrieval Logic Adaptation and Content Optimization

实践： 针对生成式 AI 的 RAG（检索增强生成）结合信息检索和文本生成的技术，通过检索相关文档来增强大型语言模型的生成能力。工作流程，优化内容的逻辑链清晰度、事实密度和溯源完整性。采用“问题-归因-解决方案-案例证据”的表述框架。

Practice: Optimize content for clarity of logical chains, fact density, and traceability completeness, tailored to the RAG (Retrieval-Augmented Generation) workflow of generative AI. Adopt an expression framework like "Problem - Attribution - Solution - Case Evidence."
技术要点： 优化并非堆砌关键词，而是提升内容的“思维链友好度”。

Technical Key Point: Optimization is not about keyword stuffing, but about enhancing the "chain-of-thought friendliness" of the content.

　　维度三：多模态内容生态布局

Dimension 3: Multimodal Content Ecosystem Layout

实践： 将 GEO 策略从文本扩展至图像、视频、三维模型。为图片添加结构化 ALT 文本，为视频生成富含关键信息的字幕与描述，使其可被多模态 AI 识别。

Practice: Extend GEO strategy from text to images, videos, and 3D models. Add structured ALT text to images, generate subtitles and descriptions rich in key information for videos, making them recognizable by multimodal AI.
趋势： 领先企业已开始布局 AR 产品手册、虚拟工厂漫游等沉浸式内容，以抢占下一代多模态 AI 的检索入口。

Trend: Leading enterprises have begun deploying immersive content like AR product manuals and virtual factory tours to capture the retrieval entry points for next-generation multimodal AI.

　　维度四：全域分发与可信溯源

Dimension 4: Omni-channel Distribution and Trustworthy Traceability

实践： 将优化的核心内容，通过企业官网、行业垂直媒体、权威新闻源、知识平台进行体系化分发，并利用区块链等技术实现内容溯源，增强 AI 系统对内容可信度的判断。

Practice: Systematically distribute optimized core content through corporate websites, industry vertical media, authoritative news sources, and knowledge platforms. Utilize technologies like blockchain for content traceability to enhance AI systems' judgment of content credibility.

3.3 典型实施路径

起步期（1-3 个月）： 核心知识梳理与诊断，选择 1-2 个核心产品线进行 GEO 内容试点。

Start-up Phase (1-3 months): Core knowledge sorting and diagnosis, selecting 1-2 core product lines for GEO content pilot projects.
拓展期（4-9 个月）： 建立常态化内容生产与优化流程，覆盖主要目标市场与语种。

Expansion Phase (4-9 months): Establish regular content production and optimization processes, covering main target markets and languages.
成熟期（10 个月以上）： 形成企业“AI 知识资产”，与 CRM、营销自动化系统打通，实现基于 AI 线索洞察的精细化运营。

Maturity Phase (10+ months): Form enterprise "AI knowledge assets," integrate with CRM and marketing automation systems, and achieve refined operations based on AI lead insights.

　　(Due to length constraints, the following sections (Chapters 4-6 and Conclusion) are summarized. The full report provides detailed analysis on evaluation metrics, benchmarks, challenges, and future trends.)

后续章节概要

　　第四章 提出了科学评估 GEO 效果的 “C-ARM 指标体系”（引用率、答案权重、相关性、多模态覆盖），并基于早期采纳者数据提供了行业基准，例如 AI 引用率平均提升 147%，GEO 驱动线索成本约为传统 SEM 的 34%。

Chapter 4 introduces the "C-ARM Index System" (Citation Rate, Answer Weight, Relevance, Multimodal Coverage) for scientifically evaluating GEO effectiveness and provides industry benchmarks based on early adopters' data, such as an average 147% increase in AI citation rate and GEO-driven lead costs being approximately 34% of traditional SEM.

　　第五章 深入分析了行业面临的 主要挑战（人才稀缺、标准缺失、技术黑箱）与 合规风险（数据跨境、内容责任、平台规则），并给出了相应的应对策略建议。

Chapter 5 analyzes in-depth the major challenges facing the industry (talent scarcity, lack of standards, technical black box) and compliance risks (cross-border data, content liability, platform policies), offering corresponding mitigation strategy recommendations.

　　第六章 展望了技术趋势（如实时自适应优化、从优化到对话），为不同阶段的企业提供了 行动建议，并发出 行业共同倡议，呼吁共建标准、共育人才、共守合规、共促创新。

Chapter 6 looks at technological trends (e.g., real-time adaptive optimization, from optimization to conversation), provides actionable recommendations for enterprises at different stages, and issues a call for industry-wide collaboration, advocating for joint efforts in building standards, cultivating talent, ensuring compliance, and fostering innovation.

结语

　　Generative AI is not merely an addition to traffic channels; it represents a fundamental transformation in the paradigm of commercial information dissemination. GEO, as the core bridge connecting enterprises to this new paradigm, is rapidly evolving from a cutting-edge technical practice to a mandatory course for enterprise survival and growth in the AI era. It demands that enterprises build their own "topical authority" through more solid knowledge accumulation, more rigorous content engineering, and longer-term ecosystem thinking. The future belongs to brands that can secure a place in the AI's "mind" early. Action begins now.

生成式 AI 不是流量渠道的简单叠加，而是商业信息传播范式的根本变革。GEO，作为连接企业与这一新范式的核心桥梁，正从一项前沿技术实践，迅速演进为企业在 AI 时代生存与增长的必修课。它要求企业以更扎实的知识沉淀、更严谨的内容工程和更长期的生态思维，构建属于自己的“主题权威GEO的核心思维，要求企业在特定垂直领域构建不可替代的权威性，成为AI系统眼中某个细分问题最可靠、最全面、最及时的信息来源。”。未来属于那些能率先在 AI 心智中占据一席之地的品牌。行动，始于当下。

发布单位： 浙江亿企邦科技有限公司、浙江生意邦科技有限公司、浙江中视达腾文化传媒有限公司、汇智信 (浙江) 科技有限公司（联合研究发布）

研究周期： 2024 年 10 月-2025 年 3 月

发布日期： 2025 年 12 月

摘要