GEO 诊断
x GEO 诊断报告
GEO 评分
11.16
内容元信息
文本模式
原始内容 vs 改写后内容
原文内容
📝 原文内容
x
CORE-EEAT 可信度评估
综合 23.45 分
E-专业度
10.0
R-相关性
20.0
C-可信度
10.5
O-客观性
64.0
维度诊断详情
L0 20 维 / L1 7 维
L0 语义基础层
语义对齐度
L0-a
问题：语义对齐分仅 50.0，与百科/学术文本差距较大
建议：增加与百科/学术/官方等高质量文本语义对齐的表述，确保首段包含关键结论
展开 1 项细分测点
语义对齐分
50
语义独特性
L0-b
问题：内容独特性不足（评分 50.0）
建议：减少段落间重复表述，增加差异化观点和独家数据
信息密度
L0-c
问题：①关键词密度仅 0.0 分，②语义结构密度仅 0.0 分
建议：增加具体数据（统计数字、百分比）、外部来源标注（'据XX报告'）、专家直接引语
展开 12 项细分测点
③统计密度
0.0
⑪步骤密度
0.0
⑦实体密度
0.0
①关键词密度
0.0
④引文密度
0.0
⑤语录密度
0.0
⑧年份事实密度
0.0
⑨对比密度
0.0
⑩定义密度
0.0
⑥声明密度
0.0
⑫引文链接有效性
50.0
②语义结构密度
0
内容时效性
L0-d
问题：②30天硬信号仅 20.0 分，③平台分层时效阈值仅 20.0 分
建议：添加'发布于/更新于'日期标记，增加近期数据和事件引用
展开 3 项细分测点
①页面可见日期
30
②30天硬信号
20.0
③平台分层时效阈值
20.0
E-E-A-T 权威度
L0-f
问题：④-c实体属性+差异化仅 0.0 分，④-f结构化数据完整度仅 0.0 分
建议：增加第一人称实践经验、专业资质认证、独立品牌段落/H2
展开 10 项细分测点
②Expertise 专业度
10
①Experience 经验信号
10
④Trustworthiness 可信度
11.0
④-b独立段落/H2
15
④-e名称一致性
80.0
④-d自传播性
10
③Authoritativeness 权威性
40.0
④-a合规信号
15
④-c实体属性+差异化
0
④-f结构化数据完整度
0.0
信息增益
L0-g
问题：信息增益不足（评分 50.0），内容与已有知识库高度重叠，建议增加独家数据和原创分析
建议：增加独家数据、原创分析和行业案例，提升信息增益
描述丰富度
L0-h
问题：侧面词覆盖率仅 0%，缺少行业、领域、场景等关键描述
建议：增加行业定位词、目标用户描述、差异化表述
查询相关性
L0-i
问题：④场景覆盖度仅 10.0 分，①语义相关性仅 25.0 分
建议：对齐目标查询语义，增加品牌词密度和搜索词覆盖
展开 4 项细分测点
②品牌词密度
100
④场景覆盖度
10.0
③搜索词覆盖
100.0
①语义相关性
25.0
文本基础规范
L0-j
问题：文本基础规范不达标
建议：确保字数≥300，段落≥3，无大段未分段文本
AI 友好结构
L0-m
问题：②结构可提取性+段落自包含性仅 0.0 分，④问句密度/千字仅 0.0 分
建议：首段直接回答核心问题，增加表格/编号列表，标题问题化
展开 5 项细分测点
⑤显式Q&A结构
20
①首段直接回答度
40
③首段铺垫率
100.0
②结构可提取性+段落自包含性
0.0
④问句密度/千字
0.0
段落可引用性与分段诊断
L0-n
问题：检测到 0 个标题，但 0 个缺少 40-60 字答案摘要
建议：在每个 H2 后添加 40-60 字自包含答案摘要
语言可读性
L0-r
问题：⑥段落长度适中度仅 15.0 分，③过渡词密度仅 20.0 分
建议：参考维度子指标优化建议进行针对性改进
展开 6 项细分测点
①平均句长
30.0
③过渡词密度
20.0
④词汇丰富度
30.0
②段落均匀度
30.0
⑥段落长度适中度
15.0
⑤句子长度分布
30.0
AI 引用风险
L0-s
问题：子维度 ③合规反投毒 表现偏低
展开 4 项细分测点
②销售词密度
100
①风险声明密度
100
④反引用信号
100
③合规反投毒
0
多模态资产
L0-u
问题：①图片Alt文本质量仅 0.0 分，②图片原创性仅 0.0 分
建议：为图片添加 Alt 文本，使用原创图片，添加视频 Schema
展开 5 项细分测点
①图片Alt文本质量
0.0
④视频Schema
0
③图片Object Schema
0
⑤视频转录结构化
0
②图片原创性
0.0
关键词堆砌
L0-v
AI引用层
L0-x
问题：AI引用层内容质量不足，缺少事实数据、引用来源和结构化表述
建议：在首段/结论前30%增加具体数据（统计数字、百分比）、来源引用和结构化标题
用户转化层
L0-y
问题：用户转化层内容质量不足，缺少品牌故事、产品优势或CTA
建议：在正文中部增加品牌叙事、产品核心优势和行动号召（CTA）
证据堆叠层
L0-z
问题：证据堆叠层内容质量不足，缺少FAQ、数据罗列或权威证明
建议：在页面底部/侧边增加FAQ板块、行业数据、证书资质和客户评价
内容情感质量
L0-emotion
展开 4 项细分测点
风险等级
3
负面/风险信号密度
0.0
正面信号密度
0.0
绝对化用语数
0
学术信任层
L0-academic
问题：①学术标识符仅 20.0 分，②引用格式仅 20.0 分
建议：参考维度子指标优化建议进行针对性改进
展开 5 项细分测点
②引用格式
20
③参考文献章节
20.0
①学术标识符
20
⑤学术术语密度
20.0
④学术信源域名
20
L1 结构规范层
文本结构
L1-a
问题：文本结构不完整（缺少 H3 层级）
建议：建立 H1-H3 标题层级，均衡段落长度，增加列表使用
内容链接
L1-b
问题：①引用链接仅 0.0 分，②导航链接仅 0.0 分
建议：参考维度子指标优化建议进行针对性改进
展开 4 项细分测点
②导航链接
0.0
①引用链接
0
③行动链接
0
④官网链接
100
格式规范
L1-c
问题：格式规范性不足
建议：增加代码块、引用块、表格、加粗等格式元素
溯源评分
L1-d
问题：官方来源仅 15.0 分，第三方研究仅 15.0 分
建议：增加官方来源、第三方研究、数据引用、学术来源等不同类别引用
展开 4 项细分测点
数据引用
15.0
学术来源
15.0
官方来源
15.0
第三方研究
15.0
逻辑闭环
L1-e
问题：逻辑闭环不完整（评分 20.0），缺少问题、分析、方案、结论环节
建议：按'问题→分析→方案→结论'四段式重构内容，确保逻辑链完整
FAQ 结构化
L1-f
问题：①FAQPage schema仅 5.0 分，②正文FAQ板块仅 10.0 分
建议：添加 FAQPage Schema 标记，或在正文中增设 FAQ 板块
展开 2 项细分测点
②正文FAQ板块
10
①FAQPage schema
5
可分发性
L1-h
问题：可分发性评估偏低
建议：增加可独立引用的数据点和结论，适配第三方媒体引用格式
诊断清单
共 29 项
品牌AI搜索可见度不足（20.16分），多个维度亟需优化
高风险（22 项）
-
高风险
侧面词覆盖率仅 0%，缺少行业、领域、场景等关键描述描述丰富度（L0-h）建议：增加行业定位词、目标用户描述、差异化表述
-
高风险
①图片Alt文本质量仅 0.0 分，②图片原创性仅 0.0 分多模态资产（L0-u）建议：为图片添加 Alt 文本，使用原创图片，添加视频 Schema
-
高风险
证据堆叠层内容质量不足，缺少FAQ、数据罗列或权威证明证据堆叠层（L0-z）建议：在页面底部/侧边增加FAQ板块、行业数据、证书资质和客户评价
-
高风险
文本结构不完整（缺少 H3 层级）文本结构（L1-a）建议：建立 H1-H3 标题层级，均衡段落长度，增加列表使用
-
高风险
结构差 + 溯源弱 = 可信度双低文本结构（L1-a）建议：重构 H1-H3 层级 + 增加引用源，提升可信度
-
高风险
格式规范性不足格式规范（L1-c）建议：增加代码块、引用块、表格、加粗等格式元素
-
高风险
文本基础规范不达标文本基础规范（L0-j）建议：确保字数≥300，段落≥3，无大段未分段文本
-
高风险
①关键词密度仅 0.0 分，②语义结构密度仅 0.0 分信息密度（L0-c）建议：增加具体数据（统计数字、百分比）、外部来源标注（'据XX报告'）、专家直接引语
-
高风险
AI 投毒/虚假宣传高风险信息密度（L0-c）建议：为量化声明补充来源标注，避免未经证实的绝对化表述
-
高风险
官方来源仅 15.0 分，第三方研究仅 15.0 分溯源评分（L1-d）建议：增加官方来源、第三方研究、数据引用、学术来源等不同类别引用
-
高风险
结构差 + 溯源弱 = 可信度双低溯源评分（L1-d）建议：重构 H1-H3 层级 + 增加引用源，提升可信度
-
高风险
可分发性评估偏低可分发性（L1-h）建议：增加可独立引用的数据点和结论，适配第三方媒体引用格式
-
高风险
①FAQPage schema仅 5.0 分，②正文FAQ板块仅 10.0 分FAQ 结构化（L1-f）建议：添加 FAQPage Schema 标记，或在正文中增设 FAQ 板块
-
高风险
④-c实体属性+差异化仅 0.0 分，④-f结构化数据完整度仅 0.0 分E-E-A-T 权威度（L0-f）建议：增加第一人称实践经验、专业资质认证、独立品牌段落/H2
-
高风险
①学术标识符仅 20.0 分，②引用格式仅 20.0 分学术信任层（L0-academic）建议：参考维度子指标优化建议进行针对性改进
-
高风险
逻辑闭环不完整（评分 20.0），缺少问题、分析、方案、结论环节逻辑闭环（L1-e）建议：按'问题→分析→方案→结论'四段式重构内容，确保逻辑链完整
-
高风险
②30天硬信号仅 20.0 分，③平台分层时效阈值仅 20.0 分内容时效性（L0-d）建议：添加'发布于/更新于'日期标记，增加近期数据和事件引用
-
高风险
⑥段落长度适中度仅 15.0 分，③过渡词密度仅 20.0 分语言可读性（L0-r）建议：参考维度子指标优化建议进行针对性改进
-
高风险
检测到 0 个标题，但 0 个缺少 40-60 字答案摘要段落可引用性与分段诊断（L0-n）建议：在每个 H2 后添加 40-60 字自包含答案摘要
-
高风险
①引用链接仅 0.0 分，②导航链接仅 0.0 分内容链接（L1-b）建议：参考维度子指标优化建议进行针对性改进
-
高风险
②结构可提取性+段落自包含性仅 0.0 分，④问句密度/千字仅 0.0 分AI 友好结构（L0-m）建议：首段直接回答核心问题，增加表格/编号列表，标题问题化
-
高风险
AI 投毒/虚假宣传高风险AI 引用风险（L0-s）建议：为量化声明补充来源标注，避免未经证实的绝对化表述
待提升（6 项）
-
待提升
④场景覆盖度仅 10.0 分，①语义相关性仅 25.0 分查询相关性（L0-i）建议：对齐目标查询语义，增加品牌词密度和搜索词覆盖
-
待提升
语义对齐分仅 50.0，与百科/学术文本差距较大语义对齐度（L0-a）建议：增加与百科/学术/官方等高质量文本语义对齐的表述，确保首段包含关键结论
-
待提升
内容独特性不足（评分 50.0）语义独特性（L0-b）建议：减少段落间重复表述，增加差异化观点和独家数据
-
待提升
信息增益不足（评分 50.0），内容与已有知识库高度重叠，建议增加独家数据和原创分析信息增益（L0-g）建议：增加独家数据、原创分析和行业案例，提升信息增益
-
待提升
AI引用层内容质量不足，缺少事实数据、引用来源和结构化表述AI引用层（L0-x）建议：在首段/结论前30%增加具体数据（统计数字、百分比）、来源引用和结构化标题
-
待提升
用户转化层内容质量不足，缺少品牌故事、产品优势或CTA用户转化层（L0-y）建议：在正文中部增加品牌叙事、产品核心优势和行动号召（CTA）
良好（1 项）
-
良好
子维度 ③合规反投毒 表现偏低AI 引用风险（L0-s）
GEO 实施方案
共 3 项策略
媒体填补策略
P1 建议
媒体结构良好，保持现有维护
- 媒体结构良好，保持现有维护
发布节奏策略
P1 建议
- 按各 AI 平台建议节奏持续输出内容，保持 AI 时效性信号
内容优化策略
P0 优先
内容维度不足——信息密度(2分)；内容时效性(24分)；E-E-A-T 权威度(19分)；描述丰富度(0分)；查询相关性(44分)；文本基础规范(0分)；AI 友好结构(35分)；段落可引用性与分段诊断(30分)；多模态资产(0分)；证据堆叠层(0分)；语言可读性(26分)；学术信任层(20分)；文本结构(0分)；内容链接(30分)；格式规范(0分)；溯源评分(8分)；逻辑闭环(20分)；FAQ 结构化(8分)；可分发性(8分)
- 信息密度：增加具体数据（统计数字、百分比）、外部来源标注（'据XX报告'）、专家直接引语
- 内容时效性：添加'发布于/更新于'日期标记，增加近期数据和事件引用
- E-E-A-T 权威度：增加第一人称实践经验、专业资质认证、独立品牌段落/H2
- 描述丰富度：增加行业定位词、目标用户描述、差异化表述
- 查询相关性：对齐目标查询语义，增加品牌词密度和搜索词覆盖
- 文本基础规范：确保字数≥300，段落≥3，无大段未分段文本
- AI 友好结构：首段直接回答核心问题，增加表格/编号列表，标题问题化
- 段落可引用性与分段诊断：在每个 H2 后添加 40-60 字自包含答案摘要
- 多模态资产：为图片添加 Alt 文本，使用原创图片，添加视频 Schema
- 证据堆叠层：在页面底部/侧边增加FAQ板块、行业数据、证书资质和客户评价
- 语言可读性：参考维度子指标优化建议进行针对性改进
- 学术信任层：参考维度子指标优化建议进行针对性改进
- 文本结构：建立 H1-H3 标题层级，均衡段落长度，增加列表使用
- 内容链接：参考维度子指标优化建议进行针对性改进
- 格式规范：增加代码块、引用块、表格、加粗等格式元素
- 溯源评分：增加官方来源、第三方研究、数据引用、学术来源等不同类别引用
- 逻辑闭环：按'问题→分析→方案→结论'四段式重构内容，确保逻辑链完整
- FAQ 结构化：添加 FAQPage Schema 标记，或在正文中增设 FAQ 板块
- 可分发性：增加可独立引用的数据点和结论，适配第三方媒体引用格式
跨维度提示汇总
已触发 2 条
2 高优先级
0 中优先级
0 低优先级
其他诊断数据
switches
web_verify:True
host_provided:True
authority_assessment:True
score_type:full
查看原始数据结构
{
"brand_name": "x",
"checklist": {
"items": [
{
"hint_id": null,
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},
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},
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},
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},
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},
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},
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},
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}
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},
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},
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}
},
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{
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{
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}
],
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"priority": "medium",
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},
{
"actions": [
{
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"text": "\u6309\u5404 AI \u5e73\u53f0\u5efa\u8bae\u8282\u594f\u6301\u7eed\u8f93\u51fa\u5185\u5bb9\uff0c\u4fdd\u6301 AI \u65f6\u6548\u6027\u4fe1\u53f7"
}
],
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"name": "\u53d1\u5e03\u8282\u594f\u7b56\u7565",
"priority": "medium",
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},
{
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{
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},
{
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},
{
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"text": "E-E-A-T \u6743\u5a01\u5ea6\uff1a\u589e\u52a0\u7b2c\u4e00\u4eba\u79f0\u5b9e\u8df5\u7ecf\u9a8c\u3001\u4e13\u4e1a\u8d44\u8d28\u8ba4\u8bc1\u3001\u72ec\u7acb\u54c1\u724c\u6bb5\u843d/H2"
},
{
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},
{
"source": "qa_dimension",
"text": "\u67e5\u8be2\u76f8\u5173\u6027\uff1a\u5bf9\u9f50\u76ee\u6807\u67e5\u8be2\u8bed\u4e49\uff0c\u589e\u52a0\u54c1\u724c\u8bcd\u5bc6\u5ea6\u548c\u641c\u7d22\u8bcd\u8986\u76d6"
},
{
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},
{
"source": "qa_dimension",
"text": "AI \u53cb\u597d\u7ed3\u6784\uff1a\u9996\u6bb5\u76f4\u63a5\u56de\u7b54\u6838\u5fc3\u95ee\u9898\uff0c\u589e\u52a0\u8868\u683c/\u7f16\u53f7\u5217\u8868\uff0c\u6807\u9898\u95ee\u9898\u5316"
},
{
"source": "qa_dimension",
"text": "\u6bb5\u843d\u53ef\u5f15\u7528\u6027\u4e0e\u5206\u6bb5\u8bca\u65ad\uff1a\u5728\u6bcf\u4e2a H2 \u540e\u6dfb\u52a0 40-60 \u5b57\u81ea\u5305\u542b\u7b54\u6848\u6458\u8981"
},
{
"source": "qa_dimension",
"text": "\u591a\u6a21\u6001\u8d44\u4ea7\uff1a\u4e3a\u56fe\u7247\u6dfb\u52a0 Alt \u6587\u672c\uff0c\u4f7f\u7528\u539f\u521b\u56fe\u7247\uff0c\u6dfb\u52a0\u89c6\u9891 Schema"
},
{
"source": "qa_dimension",
"text": "\u8bc1\u636e\u5806\u53e0\u5c42\uff1a\u5728\u9875\u9762\u5e95\u90e8/\u4fa7\u8fb9\u589e\u52a0FAQ\u677f\u5757\u3001\u884c\u4e1a\u6570\u636e\u3001\u8bc1\u4e66\u8d44\u8d28\u548c\u5ba2\u6237\u8bc4\u4ef7"
},
{
"source": "qa_dimension",
"text": "\u8bed\u8a00\u53ef\u8bfb\u6027\uff1a\u53c2\u8003\u7ef4\u5ea6\u5b50\u6307\u6807\u4f18\u5316\u5efa\u8bae\u8fdb\u884c\u9488\u5bf9\u6027\u6539\u8fdb"
},
{
"source": "qa_dimension",
"text": "\u5b66\u672f\u4fe1\u4efb\u5c42\uff1a\u53c2\u8003\u7ef4\u5ea6\u5b50\u6307\u6807\u4f18\u5316\u5efa\u8bae\u8fdb\u884c\u9488\u5bf9\u6027\u6539\u8fdb"
},
{
"source": "qa_dimension",
"text": "\u6587\u672c\u7ed3\u6784\uff1a\u5efa\u7acb H1-H3 \u6807\u9898\u5c42\u7ea7\uff0c\u5747\u8861\u6bb5\u843d\u957f\u5ea6\uff0c\u589e\u52a0\u5217\u8868\u4f7f\u7528"
},
{
"source": "qa_dimension",
"text": "\u5185\u5bb9\u94fe\u63a5\uff1a\u53c2\u8003\u7ef4\u5ea6\u5b50\u6307\u6807\u4f18\u5316\u5efa\u8bae\u8fdb\u884c\u9488\u5bf9\u6027\u6539\u8fdb"
},
{
"source": "qa_dimension",
"text": "\u683c\u5f0f\u89c4\u8303\uff1a\u589e\u52a0\u4ee3\u7801\u5757\u3001\u5f15\u7528\u5757\u3001\u8868\u683c\u3001\u52a0\u7c97\u7b49\u683c\u5f0f\u5143\u7d20"
},
{
"source": "qa_dimension",
"text": "\u6eaf\u6e90\u8bc4\u5206\uff1a\u589e\u52a0\u5b98\u65b9\u6765\u6e90\u3001\u7b2c\u4e09\u65b9\u7814\u7a76\u3001\u6570\u636e\u5f15\u7528\u3001\u5b66\u672f\u6765\u6e90\u7b49\u4e0d\u540c\u7c7b\u522b\u5f15\u7528"
},
{
"source": "qa_dimension",
"text": "\u903b\u8f91\u95ed\u73af\uff1a\u6309\u0027\u95ee\u9898\u2192\u5206\u6790\u2192\u65b9\u6848\u2192\u7ed3\u8bba\u0027\u56db\u6bb5\u5f0f\u91cd\u6784\u5185\u5bb9\uff0c\u786e\u4fdd\u903b\u8f91\u94fe\u5b8c\u6574"
},
{
"source": "qa_dimension",
"text": "FAQ \u7ed3\u6784\u5316\uff1a\u6dfb\u52a0 FAQPage Schema \u6807\u8bb0\uff0c\u6216\u5728\u6b63\u6587\u4e2d\u589e\u8bbe FAQ \u677f\u5757"
},
{
"source": "qa_dimension",
"text": "\u53ef\u5206\u53d1\u6027\uff1a\u589e\u52a0\u53ef\u72ec\u7acb\u5f15\u7528\u7684\u6570\u636e\u70b9\u548c\u7ed3\u8bba\uff0c\u9002\u914d\u7b2c\u4e09\u65b9\u5a92\u4f53\u5f15\u7528\u683c\u5f0f"
}
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