如何优化内容让AI推荐？2026年GEO七大维度分析指南

Is AI Recommending You?

If you're not optimized for LLMs, you're invisible.

发现规则已变：你为AI驱动的网络做好准备了吗？

AI在推荐你吗？

如果你没有针对大语言模型进行优化，那么你就是隐形的。

How LLMGeoKit专门用于评估网站LLM可见性的分析工具，通过7个维度提供评分和优化建议。 Works

Our process is designed to be fast, comprehensive, and actionable.

Enter Your URL
Paste any website. We fetch it and start analyzing.

输入您的网址
粘贴任意网站。我们抓取并开始分析。
We Check 7 Dimensions
Crawler access, structured data, metadata, content structure, llms.txtA standardized file format that allows website owners to communicate AI training and usage policies to AI crawlers, language models, and AI-driven search engines., citations, extractability.

我们检查7个维度
爬虫访问、结构化数据使用模式标记和其他元数据格式化的内容，以增强机器可读性，帮助AI搜索引擎更好地理解和处理信息。、元数据、内容结构、llms.txtA standardized file format that allows website owners to communicate AI training and usage policies to AI crawlers, language models, and AI-driven search engines.文件、引用信号包括作者、日期、规范URL等元数据，帮助AI评估内容的权威性和时效性，决定是否引用。、内容可提取性。
Get Your Score + Plan
Letter grade (A-F), detailed breakdown, and prioritized fixes.

获取您的评分与方案
字母等级（A-F）、详细细分报告以及优先修复建议。

The Shift Is Happening Now

For 25 years, SEO meant optimizing for Google. That era is ending. The transition from traditional search to AI-powered answer engines is not a distant future—it's underway.

转变正在发生

25年来，SEO意味着为谷歌优化。那个时代正在终结。从传统搜索向AI驱动的答案引擎的过渡并非遥远的未来——它正在进行中。

Consider these pivotal data points:

9x Higher Conversion: Conversions from AI referrals can be up to nine times higher than those from traditional search.

转化率高9倍：来自AI引荐的转化率可比传统搜索高出九倍。
The 2030 Tipping Point: By 2030, answer engines are projected to overtake traditional search as the primary method of online discovery.

2030年临界点：预计到2030年，答案引擎将超越传统搜索，成为在线发现的主要方式。
0% Prepared: An overwhelming majority of companies currently have no defined Generative Engine Optimization (GEO) strategy.

0%的准备度：绝大多数公司目前没有明确的生成式引擎优化（GEO）策略。

The Critical Blind Spot

This shift creates a fundamental blind spot for businesses:

The Single-Answer Paradigm: When a user asks an AI "What's the best [your category]?", your brand is either included in the singular, synthesized answer or it is completely invisible. There is no "page 2" to rank on.

单一答案范式：当用户询问AI“最好的[你的品类]是什么？”时，你的品牌要么被包含在那个单一的、综合生成的答案中，要么就完全隐形。这里没有可以排名的“第二页”。
The Scrolling Disappearance: Unlike traditional search results pages (SERPs), there is often no list to scroll through. The interaction is conversational, and visibility is binary: you are either cited or you are not.

滚动条的消失：与传统搜索结果页不同，通常没有可供浏览的列表。交互是对话式的，可见性是二元的：你要么被引用，要么没有。
The Strategy Gap: Most organizations lack the knowledge and framework to systematically secure a place in these AI-generated answers, leaving a massive opportunity untapped.

策略鸿沟：大多数组织缺乏系统性地在AI生成的答案中确保一席之地的知识和框架，让巨大的机遇白白流失。

SEO vs. GEO: A Fundamental Game Change

To navigate this new landscape, it's crucial to understand the distinction between the old rules and the new.

SEO (Search Engine Optimization) optimizes for Google's index. It focuses on keywords, backlinks, and page speed. The competition is for a position among ten blue links on a search results page.

SEO（搜索引擎优化） 针对谷歌的索引进行优化。它侧重于关键词、反向链接和页面速度。竞争目标是在搜索结果页的十个蓝色链接中获取一个位置。
GEO (Generative Engine Optimization) optimizes for AI answers. It prioritizes structured data, citation signals, and machine-readable content. The competition is for a single, authoritative mention within a generative AI's response.

GEO（生成式引擎优化）GEO（Generative Engine Optimization）是一种通过优化品牌信息、内容和数据，以提高生成式人工智能（如大语言模型）对品牌认知、理解和推荐准确性的技术与策略。针对AI答案进行优化。它优先考虑结构化数据使用模式标记和其他元数据格式化的内容，以增强机器可读性，帮助AI搜索引擎更好地理解和处理信息。、引用信号包括作者、日期、规范URL等元数据，帮助AI评估内容的权威性和时效性，决定是否引用。和机器可读的内容。竞争目标是在生成式AI的回复中获得一个单一的、权威的提及。

The game, the rules, and the playing field have changed. However, the ultimate outcome remains starkly similar: visibility or invisibility.

游戏、规则和竞技场都已改变。然而，最终的结果依然惊人地相似：可见或隐形。

What LLMs and Answer Engines Look For

AI models don't "read" web pages like humans do; they parse and analyze them to extract trustworthy information. To be citable, your content must be easily discoverable, understandable, and verifiable by these systems. Key signals include:

Structured Data They Can Parse: Rich, unambiguous data formats like JSON-LD using schema.org vocabularies (e.g., FAQPage, HowTo, Article).

它们能解析的结构化数据使用模式标记和其他元数据格式化的内容，以增强机器可读性，帮助AI搜索引擎更好地理解和处理信息。：使用schema.org词汇表（如FAQPage、HowTo、Article）的丰富、明确的数据格式，如JSON-LD。
Strong Citation Signals: Clear authorship, publication dates, canonical URLs, and language tags that establish content authority and freshness.

强有力的引用信号包括作者、日期、规范URL等元数据，帮助AI评估内容的权威性和时效性，决定是否引用。：明确的内容作者、发布日期、规范URL和语言标签，用以建立内容权威性和新鲜度。
Clear Content Hierarchy: Proper use of semantic HTML (H1-H6 headings, lists, tables) that logically organizes information for machine comprehension.

清晰的内容层级：正确使用语义化HTML（H1-H6标题、列表、表格），以逻辑化地组织信息，便于机器理解。
Unobstructed Crawler Access: Permissive robots.txt rules and the emerging llms.txt standard to explicitly guide AI crawlers.

畅通的爬虫访问：宽松的robots.txt规则以及新兴的llms.txt标准，用以明确引导AI爬虫。
Easily Extractable Facts: Well-defined FAQs, data tables, definitions, and disclosures that can be cleanly pulled into an AI's knowledge synthesis.

易于提取的事实：定义清晰的常见问题解答、数据表格、定义和披露声明，这些内容可以被清晰地提取到AI的知识合成中。

The 7 Dimensions of LLM Visibility

Our framework evaluates your website's readiness across seven core dimensions. Each contributes decisively to whether an AI can find, understand, trust, and ultimately cite your content.

Foundation & Access

Robots.txt & Crawlability: Rules for web crawlers, sitemap references, and specific directives for LLM crawlers.

Robots.txt与可爬取性：针对网络爬虫的规则、网站地图引用以及对LLM爬虫的特定指令。
Structured Data: Implementation of JSON-LD using schema.org markup, particularly for Q&A and instructional content (FAQ, HowTo).

结构化数据使用模式标记和其他元数据格式化的内容，以增强机器可读性，帮助AI搜索引擎更好地理解和处理信息。：使用schema.org标记的JSON-LD实现，特别是针对问答和指导性内容（FAQ、HowTo）。
Metadata: Optimization of title tags, meta descriptions, and Open Graph tags for accurate context summarization.

元数据：优化标题标签、元描述和Open Graph标签，以实现准确的上下文摘要。
Content Structure: Use of semantic HTML headings, lists, and tables to create a machine-navigable information architecture.

内容结构：使用语义化HTML标题、列表和表格，以创建机器可导航的信息架构。

Authority & Extraction

llms.txtA standardized file format that allows website owners to communicate AI training and usage policies to AI crawlers, language models, and AI-driven search engines.: Deployment of this emerging standard file to provide explicit guidance to LLM and AI crawlers.

llms.txtA standardized file format that allows website owners to communicate AI training and usage policies to AI crawlers, language models, and AI-driven search engines.文件：部署这一新兴的标准文件，为LLM和AI爬虫提供明确的指导。
Citation Readiness: Presence of clear author attribution, publication/modification dates, canonical URLs, and language metadata.

引用就绪度：是否存在明确的内容作者归属、发布/修改日期、规范URL和语言元数据。
Extractability: The ease with which key factual content—like FAQs, data tables, and definitions—can be identified and extracted by machines.

可提取性：关键事实内容（如常见问题解答、数据表格和定义）能被机器识别和提取的难易程度。

Find Your Blind Spots

You can't fix what you can't measure. Our diagnostic tool provides a clear, immediate assessment across all seven dimensions in about 30 seconds, giving you a precise understanding of what's helping and hurting your visibility to AI.

你无法修复无法衡量的东西。我们的诊断工具能在约30秒内，在所有七个维度上提供清晰、即时评估，让你精确了解哪些因素有助于或损害你在AI眼中的可见性。

Ready to see your score? [Scan Your Site Free]

准备好查看你的评分了吗？ [免费扫描你的网站]

Need expert guidance to implement changes? Our consulting team can conduct a deep-dive audit, execute technical fixes, and help you develop a comprehensive, forward-looking GEO strategy. [View Consulting Services]

需要专家指导来实施更改？我们的咨询团队可以进行深度审计、执行技术修复，并帮助您制定全面、前瞻性的GEO策略。 [查看咨询服务]