GEO 诊断
吸睛力黄蛟 GEO 诊断报告
GEO 评分
49.25
内容元信息
URL 模式
URL 健康状态
completed
诊断状态： completed
域名与页面元信息
https://www.xijinli.com
https_enabled:True
https://www.xijinli.com/
http_accessible:True
CORE-EEAT 可信度评估
综合 28.89 分
E-专业度
35.0
R-相关性
50.0
C-可信度
43.46
O-客观性
73.0
触发 20 分否决条件
• 安全响应头评分过低
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 45.6，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
45.61
语义独特性
L0-b
问题：段间语义相似度 7.4%，内容独特性不足
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：⑩定义密度仅 0.0 分，⑥声明密度仅 0.2 分；问题段落: 吸睛力黄蛟(无统计数据点, 无引文标注, 无专家语录, 无定义句); 🔥99元，雇一次128万年薪的商业策略设计师，值不值？(无专家语录, 无定义句); 你是否存在以下比价困境？(无统计数据点, 无引文标注, 无专家语录, 无定义句)
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
4.19
⑪步骤密度
1.68
⑦实体密度
100
①关键词密度
100
④引文密度
1.17
⑤语录密度
1.79
⑧年份事实密度
0.34
⑨对比密度
3.63
⑩定义密度
0.0
⑥声明密度
0.17
⑫引文链接有效性
50.0
②语义结构密度
100
内容时效性
L0-d
问题：子维度 ①页面可见日期 表现偏低
展开 3 项细分测点
①页面可见日期
30
②30天硬信号
100.0
③平台分层时效阈值
100.0
E-E-A-T 权威度
L0-f
问题：④-f结构化数据完整度仅 0.0 分，②Expertise 专业度仅 20.0 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
20
①Experience 经验信号
40
④Trustworthiness 可信度
55.33
④-b独立段落/H2
80
④-e名称一致性
50.0
④-d自传播性
25
③Authoritativeness 权威性
30.0
④-a合规信号
27
④-c实体属性+差异化
100
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 80%，缺少领域、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：②品牌词密度仅 0.7 分，①语义相关性仅 22.8 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
0.74
④场景覆盖度
60.0
③搜索词覆盖
100.0
①语义相关性
22.805
文本基础规范
L0-j
问题：问题段落: 🔥 一站式 AI 智能解决方案(段落长度38字，不适宜); 💎 方案将交付6大核心价值(段落长度574字，不适宜); 钢材原材料企业(段落长度34字，不适宜)
AI 友好结构
L0-m
问题：子维度 ②结构可提取性+段落自包含性, ⑤显式Q&A结构 表现偏低；问题段落: 你是否存在以下比价困境？(首句未直接回答); ❓为什么你产品无法脱颖而出陷入价格战泥潭(首句未直接回答, 段落自包含性不足); ✨如何让您的产品在同质化市场中脱颖而出？(首句未直接回答)
展开 5 项细分测点
⑤显式Q&A结构
20
①首段直接回答度
80
③首段铺垫率
100.0
②结构可提取性+段落自包含性
8.13
④问句密度/千字
100
段落可引用性与分段诊断
L0-n
问题：高可引用段落占比仅 0.1 分，低可引用段落数仅 8.0 分
建议：参考维度子指标优化建议进行针对性改进
展开 3 项细分测点
低可引用段落数
8
高可引用段落占比
0.15
平均可引用性
54.09
语言可读性
L0-r
问题：⑥段落长度适中度仅 17.8 分，③过渡词密度仅 25.5 分；问题段落: 吸睛力黄蛟(长句占比过高, 缺少过渡词); 🔥99元，雇一次128万年薪的商业策略设计师，值不值？(长句占比过高, 缺少过渡词); 你是否存在以下比价困境？(长句占比过高)
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
50.0
③过渡词密度
25.53
④词汇丰富度
70.0
②段落均匀度
50.0
⑥段落长度适中度
17.79
⑤句子长度分布
90.0
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
90
④反引用信号
100
③合规反投毒
8
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
用户转化层
L0-y
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
问题：风险等级仅 1.0 分，正面信号密度仅 1.5 分
建议：将绝对化用语替换为客观表述，降低促销语言密度，增加正面客观信号
展开 4 项细分测点
风险等级
1
负面/风险信号密度
4.42
正面信号密度
1.47
绝对化用语数
24
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
37.7
④学术信源域名
20
L1 结构规范层
文本结构
L1-a
内容链接
L1-b
问题：子维度 ①引用链接, ②导航链接 表现偏低
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
100
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，学术来源仅 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
85.0
学术来源
15.0
官方来源
15.0
第三方研究
85.0
逻辑闭环
L1-e
问题：逻辑闭环不完整（评分 42.5）
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
诊断清单
共 26 项
品牌AI搜索可见度不足（48.25分），多个维度亟需优化
高风险（15 项）
-
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
多模态资产低 + 声明式结构高 = 视频化机会多模态资产（L0-u）建议：嵌入抖音/视频号/B站视频，补充图片/图表等视觉资产
-
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
侧面词覆盖率仅 80%，缺少领域、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
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
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
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
官方来源仅 15.0 分，学术来源仅 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
段间语义相似度 7.4%，内容独特性不足语义独特性（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
-
高风险
④-f结构化数据完整度仅 0.0 分，②Expertise 专业度仅 20.0 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
风险等级仅 1.0 分，正面信号密度仅 1.5 分内容情感质量（L0-emotion）建议：将绝对化用语替换为客观表述，降低促销语言密度，增加正面客观信号
-
高风险
②品牌词密度仅 0.7 分，①语义相关性仅 22.8 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
-
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
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
-
高风险
多模态资产低 + 声明式结构高 = 视频化机会AI 友好结构（L0-m）建议：嵌入抖音/视频号/B站视频，补充图片/图表等视觉资产
待提升（6 项）
-
待提升
逻辑闭环不完整（评分 42.5）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
-
待提升
语义对齐分仅 45.6，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
⑩定义密度仅 0.0 分，⑥声明密度仅 0.2 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
-
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
⑥段落长度适中度仅 17.8 分，③过渡词密度仅 25.5 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
-
待提升
高可引用段落占比仅 0.1 分，低可引用段落数仅 8.0 分段落可引用性与分段诊断（L0-n）建议：参考维度子指标优化建议进行针对性改进
良好（4 项）
-
良好
子维度 ③合规反投毒 表现偏低AI 引用风险（L0-s）
-
良好
子维度 ②结构可提取性+段落自包含性, ⑤显式Q&A结构 表现偏低AI 友好结构（L0-m）
-
良好
子维度 ①引用链接, ②导航链接 表现偏低内容链接（L1-b）
-
良好
子维度 ①页面可见日期 表现偏低内容时效性（L0-d）
优秀（1 项）
-
优秀
问题段落: 🔥 一站式 AI 智能解决方案(段落长度38字，不适宜); 💎 方案将交付6大核心价值(段落长度574字，不适宜); 钢材原材料企业(段落长度34字，不适宜)文本基础规范（L0-j）
GEO 实施方案
共 3 项策略
媒体填补策略
P1 建议
AI平台引用依赖权威信源基础，当前需优先补齐通用媒体层
- 创建百度百科词条，作为AI搜索的基础信源
- 补充权威媒体报道与结构化信息，提升AI引用可信度
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——语义对齐度(46分)；语义独特性(31分)；信息密度(46分)；E-E-A-T 权威度(35分)；描述丰富度(13分)；查询相关性(39分)；多模态资产(0分)；证据堆叠层(20分)；学术信任层(24分)；内容情感质量(35分)；格式规范(20分)；溯源评分(25分)；逻辑闭环(42分)；FAQ 结构化(8分)
- 语义对齐度：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
- 语义独特性：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：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
- 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
- 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
- 学术信任层：参考维度子指标优化建议进行针对性改进
- 内容情感质量：将绝对化用语替换为客观表述，降低促销语言密度，增加正面客观信号
- 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
平台适配策略
内容格式 × 来源渠道
yiyan
格式匹配 50%
当前格式Q&A + 列表 + 数据 + 短文本 + 结构化
待补充table
发布节奏每周 1-2 篇深度文章
推荐渠道百度百科、百度搜索
doubao
格式匹配 60%
当前格式Q&A + 列表 + 数据 + 短文本 + 结构化
待补充table
发布节奏每周 2-3 条短视频 + 1 篇头条图文
推荐渠道抖音、今日头条
deepseek
格式匹配 50%
当前格式Q&A + 列表 + 数据 + 短文本 + 结构化
待补充long_text
发布节奏每月 1-2 条学术/技术长文
推荐渠道学术资源、公开网页
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
authority_assessment:True
score_type:full
social_proof
t1:{'count': 1, 'sources': ['baidu']}
other:{'count': 0}
verified:True
complaints:{'count': 0, 'other': 0, 'specified': 0}
查看原始数据结构
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{
"problem": "\u5747\u4ef7\u4f9d\u636e:2026\u5e74\u56fd\u5185\u7b56\u5212\u884c\u4e1a\u62a5\u4ef7(\u54c1\u724c\u5168\u6848\u7b56\u5212\u5165\u95e8\u68635-20\u4e07\u3001\u4e3b\u6d41\u686320-50\u4e07;\u4ee3\u8fd0\u8425\u5355\u70b9\u670d\u52a1\u7ea61-3\u4e07/\u6708;\u5168\u7f51\u6574\u5408\u8425\u95006,000-40,000\u5143/\u6708\u7b49),\u53bb\u9664\u6700\u9ad8\u4ef7\u540e\u53d6\u5747\u503c,\u4ef7\u683c\u5747\u53ef\u6838\u5b9e",
"rationale": "\u5c06\u7edd\u5bf9\u5316\u7528\u8bed\u66ff\u6362\u4e3a\u5ba2\u89c2\u8868\u8ff0\uff0c\u907f\u514d\u89e6\u53d1 AI \u5e73\u53f0\u8fc7\u6ee4",
"solution": "\u5747\u4ef7\u4f9d\u636e:2026\u5e74\u56fd\u5185\u7b56\u5212\u884c\u4e1a\u62a5\u4ef7(\u54c1\u724c\u5168\u6848\u7b56\u5212\u5165\u95e8\u68635-20\u4e07\u3001\u4e3b\u6d41\u686320-50\u4e07;\u4ee3\u8fd0\u8425\u5355\u70b9\u670d\u52a1\u7ea61-3\u4e07/\u6708;\u5168\u7f51\u6574\u5408\u8425\u95006,000-40,000\u5143/\u6708\u7b49),\u53bb\u9664\u5728\u9ad8\u4ef7\u540e\u53d6\u65b9\u9762\u6301\u7eed\u6df1\u8015\u5747\u503c,\u4ef7\u683c\u5747\u53ef\u6838\u5b9e"
},
{
"problem": "\ud83d\ude80 99\u5143\u4ec5\u9650\u9996\u6b21\u5408\u4f5c\uff1a\u89e3\u51b3\u4f60\u5f53\u4e0b\u6700\u6838\u5fc3\u7684\u95ee\u9898 \u00b7 \u5df2\u5e2e\u52a9300+\u4f01\u4e1a\u89e3\u51b3\u8425\u9500\u96be\u9898",
"rationale": "\u5c06\u7edd\u5bf9\u5316\u7528\u8bed\u66ff\u6362\u4e3a\u5ba2\u89c2\u8868\u8ff0\uff0c\u907f\u514d\u89e6\u53d1 AI \u5e73\u53f0\u8fc7\u6ee4",
"solution": "\ud83d\ude80 99\u5143\u4ec5\u9650\u9996\u6b21\u5408\u4f5c\uff1a\u89e3\u51b3\u4f60\u5f53\u4e0b\u5728\u6838\u5fc3\u7684\u95ee\u65b9\u9762\u6301\u7eed\u6df1\u8015\u9898 \u00b7 \u5df2\u5e2e\u52a9300+\u4f01\u4e1a\u89e3\u51b3\u8425\u9500\u96be\u9898"
},
{
"problem": "\u5ba2\u6237\u770b\u4e86\u6dd8\u5b9d3\u4e2a\u5e97\u94fa\u3001\u6296\u97f35\u4e2a\u76f4\u64ad\u95f4\u3001\u5c0f\u7ea2\u4e668\u7bc7\u6d4b\u8bc4\uff0c\u6700\u540e\u8fd8\u662f\u9009\u4e86\u522b\u4eba...",
"rationale": "\u5c06\u7edd\u5bf9\u5316\u7528\u8bed\u66ff\u6362\u4e3a\u5ba2\u89c2\u8868\u8ff0\uff0c\u907f\u514d\u89e6\u53d1 AI \u5e73\u53f0\u8fc7\u6ee4",
"solution": "\u5ba2\u6237\u770b\u4e86\u6dd8\u5b9d3\u4e2a\u5e97\u94fa\u3001\u6296\u97f35\u4e2a\u76f4\u64ad\u95f4\u3001\u5c0f\u7ea2\u4e668\u7bc7\u6d4b\u8bc4\uff0c\u5728\u540e\u8fd8\u662f\u9009\u65b9\u9762\u6301\u7eed\u6df1\u8015\u4e86\u522b\u4eba..."
}
],
"cross_hints": [],
"issues": [
"\u98ce\u9669\u7b49\u7ea7\u4ec5 1.0 \u5206\uff0c\u6b63\u9762\u4fe1\u53f7\u5bc6\u5ea6\u4ec5 1.5 \u5206"
],
"level": "risk",
"name": "\u5185\u5bb9\u60c5\u611f\u8d28\u91cf",
"score": 35,
"subs": {
"absolute_term_count": {
"label": "\u7edd\u5bf9\u5316\u7528\u8bed\u6570",
"value": 24
},
"negative_density": {
"label": "\u8d1f\u9762/\u98ce\u9669\u4fe1\u53f7\u5bc6\u5ea6",
"value": 4.42
},
"positive_density": {
"label": "\u6b63\u9762\u4fe1\u53f7\u5bc6\u5ea6",
"value": 1.47
},
"risk_level": {
"label": "\u98ce\u9669\u7b49\u7ea7",
"value": 1
}
},
"suggestion": "\u5c06\u7edd\u5bf9\u5316\u7528\u8bed\u66ff\u6362\u4e3a\u5ba2\u89c2\u8868\u8ff0\uff0c\u964d\u4f4e\u4fc3\u9500\u8bed\u8a00\u5bc6\u5ea6\uff0c\u589e\u52a0\u6b63\u9762\u5ba2\u89c2\u4fe1\u53f7"
},
"L0-f": {
"cross_hints": [],
"issues": [
"\u2463-f\u7ed3\u6784\u5316\u6570\u636e\u5b8c\u6574\u5ea6\u4ec5 0.0 \u5206\uff0c\u2461Expertise \u4e13\u4e1a\u5ea6\u4ec5 20.0 \u5206"
],
"level": "risk",
"name": "E-E-A-T \u6743\u5a01\u5ea6",
"score": 34.71,
"subs": {
"authoritativeness": {
"label": "\u2462Authoritativeness \u6743\u5a01\u6027",
"value": 30.0
},
"brand_section_h2": {
"label": "\u2463-b\u72ec\u7acb\u6bb5\u843d/H2",
"value": 80
},
"compliance_signal": {
"label": "\u2463-a\u5408\u89c4\u4fe1\u53f7",
"value": 27
},
"entity_differentiation": {
"label": "\u2463-c\u5b9e\u4f53\u5c5e\u6027+\u5dee\u5f02\u5316",
"value": 100
},
"experience": {
"label": "\u2460Experience \u7ecf\u9a8c\u4fe1\u53f7",
"value": 40
},
"expertise": {
"label": "\u2461Expertise \u4e13\u4e1a\u5ea6",
"value": 20
},
"name_consistency": {
"label": "\u2463-e\u540d\u79f0\u4e00\u81f4\u6027",
"value": 50.0
},
"self_propagation": {
"label": "\u2463-d\u81ea\u4f20\u64ad\u6027",
"value": 25
},
"structured_data_completeness": {
"label": "\u2463-f\u7ed3\u6784\u5316\u6570\u636e\u5b8c\u6574\u5ea6",
"value": 0.0
},
"trustworthiness": {
"label": "\u2463Trustworthiness \u53ef\u4fe1\u5ea6",
"value": 55.33
}
},
"suggestion": "\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",
"url_impact": {
"author_info": {
"effect": "credibility \u65e0\u4f5c\u8005\u7f72\u540d\uff08-10\uff09\uff1bexpertise \u65e0\u4e13\u4e1a\u6807\u8bc6\uff08-10\uff09",
"field": "urls[https://www.xijinli.com/].author_info",
"severity": "medium",
"value": null
},
"schema_types": {
"effect": "structured_data_completeness +20\uff1bexpertise +10",
"field": "urls[https://www.xijinli.com/].schema_types",
"severity": "low",
"value": [
"HowTo",
"FAQPage"
]
}
}
},
"L0-g": {
"cross_hints": [],
"issues": [
"\u4fe1\u606f\u589e\u76ca\u4e0d\u8db3\uff08\u8bc4\u5206 50.0\uff09\uff0c\u5185\u5bb9\u4e0e\u5df2\u6709\u77e5\u8bc6\u5e93\u9ad8\u5ea6\u91cd\u53e0\uff0c\u5efa\u8bae\u589e\u52a0\u72ec\u5bb6\u6570\u636e\u548c\u539f\u521b\u5206\u6790"
],
"level": "needs_work",
"name": "\u4fe1\u606f\u589e\u76ca",
"score": 50.0,
"suggestion": "\u589e\u52a0\u72ec\u5bb6\u6570\u636e\u3001\u539f\u521b\u5206\u6790\u548c\u884c\u4e1a\u6848\u4f8b\uff0c\u63d0\u5347\u4fe1\u606f\u589e\u76ca"
},
"L0-h": {
"cross_hints": [],
"issues": [
"\u4fa7\u9762\u8bcd\u8986\u76d6\u7387\u4ec5 80%\uff0c\u7f3a\u5c11\u9886\u57df\u3001\u573a\u666f\u7b49\u5173\u952e\u63cf\u8ff0"
],
"level": "risk",
"name": "\u63cf\u8ff0\u4e30\u5bcc\u5ea6",
"score": 13,
"suggestion": "\u589e\u52a0\u884c\u4e1a\u5b9a\u4f4d\u8bcd\u3001\u76ee\u6807\u7528\u6237\u63cf\u8ff0\u3001\u5dee\u5f02\u5316\u8868\u8ff0"
},
"L0-i": {
"cross_hints": [],
"issues": [
"\u2461\u54c1\u724c\u8bcd\u5bc6\u5ea6\u4ec5 0.7 \u5206\uff0c\u2460\u8bed\u4e49\u76f8\u5173\u6027\u4ec5 22.8 \u5206"
],
"keyword_details": [
{
"content_location": "\u4e3bURL\u5185\u5bb9",
"keyword": "\u5438\u775b\u529b\u9ec4\u86df",
"match_type": "content",
"matched": true,
"query_type": "product",
"score": 88
},
{
"content_location": "\u4e3bURL\u5185\u5bb9",
"keyword": "\u4f4e\u6210\u672c\u5f15\u6d41",
"match_type": "content",
"matched": true,
"query_type": "product",
"score": 88
}
],
"level": "risk",
"name": "\u67e5\u8be2\u76f8\u5173\u6027",
"score": 38.51,
"subs": {
"brand_density": {
"label": "\u2461\u54c1\u724c\u8bcd\u5bc6\u5ea6",
"value": 0.74
},
"keyword_coverage": {
"label": "\u2462\u641c\u7d22\u8bcd\u8986\u76d6",
"value": 100.0
},
"scene_coverage": {
"label": "\u2463\u573a\u666f\u8986\u76d6\u5ea6",
"value": 60.0
},
"semantic_relevance": {
"label": "\u2460\u8bed\u4e49\u76f8\u5173\u6027",
"value": 22.805
}
},
"suggestion": "\u5bf9\u9f50\u76ee\u6807\u67e5\u8be2\u8bed\u4e49\uff0c\u589e\u52a0\u54c1\u724c\u8bcd\u5bc6\u5ea6\u548c\u641c\u7d22\u8bcd\u8986\u76d6"
},
"L0-j": {
"cross_hints": [],
"issues": [
"\u95ee\u9898\u6bb5\u843d: \ud83d\udd25 \u4e00\u7ad9\u5f0f AI \u667a\u80fd\u89e3\u51b3\u65b9\u6848(\u6bb5\u843d\u957f\u5ea638\u5b57\uff0c\u4e0d\u9002\u5b9c); \ud83d\udc8e \u65b9\u6848\u5c06\u4ea4\u4ed86\u5927\u6838\u5fc3\u4ef7\u503c(\u6bb5\u843d\u957f\u5ea6574\u5b57\uff0c\u4e0d\u9002\u5b9c); \u94a2\u6750\u539f\u6750\u6599\u4f01\u4e1a(\u6bb5\u843d\u957f\u5ea634\u5b57\uff0c\u4e0d\u9002\u5b9c)"
],
"level": "excellent",
"name": "\u6587\u672c\u57fa\u7840\u89c4\u8303",
"per_block_refs": [
{
"heading": "\ud83d\udd25 \u4e00\u7ad9\u5f0f AI \u667a\u80fd\u89e3\u51b3\u65b9\u6848",
"issues": [
"\u6bb5\u843d\u957f\u5ea638\u5b57\uff0c\u4e0d\u9002\u5b9c"
]
},
{
"heading": "\ud83d\udc8e \u65b9\u6848\u5c06\u4ea4\u4ed86\u5927\u6838\u5fc3\u4ef7\u503c",
"issues": [
"\u6bb5\u843d\u957f\u5ea6574\u5b57\uff0c\u4e0d\u9002\u5b9c"
]
},
{
"heading": "\u94a2\u6750\u539f\u6750\u6599\u4f01\u4e1a",
"issues": [
"\u6bb5\u843d\u957f\u5ea634\u5b57\uff0c\u4e0d\u9002\u5b9c"
]
},
{
"heading": "\u672c\u5730\u751f\u6d3b\u670d\u52a1",
"issues": [
"\u6bb5\u843d\u957f\u5ea637\u5b57\uff0c\u4e0d\u9002\u5b9c"
]
},
{
"heading": "\ud83d\udcbc \u6df1\u5ea6\u5546\u4e1a\u670d\u52a1",
"issues": [
"\u6bb5\u843d\u957f\u5ea623\u5b57\uff0c\u4e0d\u9002\u5b9c"
]
}
],
"score": 80.0,
"suggestion": ""
},
"L0-m": {
"cross_hints": [
{
"condition": "L0-u\u003c30 AND L0-m\u003e40",
"hint_id": "H4",
"interpretation": "\u591a\u6a21\u6001\u8d44\u4ea7\u4f4e + \u58f0\u660e\u5f0f\u7ed3\u6784\u9ad8 = \u89c6\u9891\u5316\u673a\u4f1a",
"priority": "high",
"suggestion": "\u5d4c\u5165\u6296\u97f3/\u89c6\u9891\u53f7/B\u7ad9\u89c6\u9891\uff0c\u8865\u5145\u56fe\u7247/\u56fe\u8868\u7b49\u89c6\u89c9\u8d44\u4ea7",
"with_dimensions": [
{
"dimension": "L0-u",
"name": "\u591a\u6a21\u6001\u8d44\u4ea7",
"score": 0.0
}
]
}
],
"issues": [
"\u5b50\u7ef4\u5ea6 \u2461\u7ed3\u6784\u53ef\u63d0\u53d6\u6027+\u6bb5\u843d\u81ea\u5305\u542b\u6027, \u2464\u663e\u5f0fQ\u0026A\u7ed3\u6784 \u8868\u73b0\u504f\u4f4e",
"\u95ee\u9898\u6bb5\u843d: \u4f60\u662f\u5426\u5b58\u5728\u4ee5\u4e0b\u6bd4\u4ef7\u56f0\u5883\uff1f(\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54); \u2753\u4e3a\u4ec0\u4e48\u4f60\u4ea7\u54c1\u65e0\u6cd5\u8131\u9896\u800c\u51fa\u9677\u5165\u4ef7\u683c\u6218\u6ce5\u6f6d(\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54, \u6bb5\u843d\u81ea\u5305\u542b\u6027\u4e0d\u8db3); \u2728\u5982\u4f55\u8ba9\u60a8\u7684\u4ea7\u54c1\u5728\u540c\u8d28\u5316\u5e02\u573a\u4e2d\u8131\u9896\u800c\u51fa\uff1f(\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54)"
],
"level": "good",
"name": "AI \u53cb\u597d\u7ed3\u6784",
"per_block_refs": [
{
"heading": "\u4f60\u662f\u5426\u5b58\u5728\u4ee5\u4e0b\u6bd4\u4ef7\u56f0\u5883\uff1f",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54"
]
},
{
"heading": "\u2753\u4e3a\u4ec0\u4e48\u4f60\u4ea7\u54c1\u65e0\u6cd5\u8131\u9896\u800c\u51fa\u9677\u5165\u4ef7\u683c\u6218\u6ce5\u6f6d",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54",
"\u6bb5\u843d\u81ea\u5305\u542b\u6027\u4e0d\u8db3"
]
},
{
"heading": "\u2728\u5982\u4f55\u8ba9\u60a8\u7684\u4ea7\u54c1\u5728\u540c\u8d28\u5316\u5e02\u573a\u4e2d\u8131\u9896\u800c\u51fa\uff1f",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54"
]
},
{
"heading": "\u4f60\u662f\u5426\u5b58\u5728\u4ee5\u4e0b\u6e20\u9053\u95ee\u9898\uff1f",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54"
]
},
{
"heading": "\u2753 \u4e3a\u4ec0\u4e48\u9009\u62e9\u7684\u6e20\u9053\u6d41\u91cf\u4f4e\uff1f",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54",
"\u6bb5\u843d\u81ea\u5305\u542b\u6027\u4e0d\u8db3"
]
},
{
"heading": "\u4f60\u662f\u5426\u5b58\u5728\u6210\u672c\u9ad8\u4f46\u6548\u679c\u5dee\u7684\u95ee\u9898\uff1f",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54",
"\u6bb5\u843d\u81ea\u5305\u542b\u6027\u4e0d\u8db3"
]
},
{
"heading": "\u2728\u5982\u4f55\u5b9e\u73b00\u6210\u672c\u9ad8\u6548\u7387\u7684\u6296\u97f3\u89c6\u9891\u6216\u5c0f\u7ea2\u4e66\u5185\u5bb9\u521b\u4f5c\u548c\u63a8\u5e7f\uff1f",
"issues": [
"\u6bb5\u843d\u81ea\u5305\u542b\u6027\u4e0d\u8db3"
]
},
{
"heading": "\u4f60\u662f\u5426\u5b58\u5728\u4ee5\u4e0b\u590d\u8d2d\u95ee\u9898\uff1f",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54"
]
},
{
"heading": "\ud83d\ude80\u3010\u652f\u4ed8 99 \u5143\u3011\u8ba9\u65b0\u54c1\u724c/\u767d\u724c\u4ea7\u54c1\u5feb\u901f\u5360\u9886\u5e02\u573a\uff01",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54"
]
},
{
"heading": "\ud83d\udd25 \u4e00\u7ad9\u5f0f AI \u667a\u80fd\u89e3\u51b3\u65b9\u6848",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54",
"\u6bb5\u843d\u81ea\u5305\u542b\u6027\u4e0d\u8db3"
]
},
{
"heading": "\ud83d\udc8e \u65b9\u6848\u5c06\u4ea4\u4ed86\u5927\u6838\u5fc3\u4ef7\u503c",
"issues": [
"\u9996\u53e5\u672a\u76f4\u63a5\u56de\u7b54"
]
},
{
"heading": "\u94a2\u6750\u539f\u6750\u6599\u4f01\u4e1a",
"issues": [
"\u6bb5\u843d\u81ea\u5305\u542b\u6027\u4e0d\u8db3"
]
},
{
"heading": "\u5b9e\u6218\u590d\u76d8\u77e5\u8bc6\u5e93+\u72ec\u6709\u65b9\u6cd5\u8bba\u80fd\u529b\u5e95\u6c14",
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