E-E-A-T 与品牌权威建设

Google 的 E-E-A-T（经验、专业性、权威性、可信度）不是排名算法，而是质量评估框架——但不满足它的内容很难在竞争激烈的领域长期排名。

E-E-A-T 构成

品牌权威评估

"""
E-E-A-T 品牌权威评估框架
"""
from dataclasses import dataclass, field
@dataclass
class BrandSignals:
"""品牌权威信号"""
# Experience — 经验
case_studies: int           # 有具体数据的案例研究数量
original_research: int      # 原创研究/调查数
product_reviews_with_photos: int  # 带实物图的评测
# Expertise — 专业性
author_bios_complete: bool  # 作者简介是否完整（含资质）
certifications: list[str]   # 行业认证列表
expert_contributors: int    # 外部专家贡献者数
# Authoritativeness — 权威性
referring_domains: int      # 指向域名数
media_mentions: int         # 主流媒体引用次数（过去12月）
wikipedia_mentions: bool    # 是否被维基百科引用
industry_awards: int        # 行业奖项
# Trustworthiness — 可信度
https_enabled: bool
privacy_policy: bool
contact_page: bool
reviews_avg: float          # 平均评分 (1-5)
reviews_count: int
class EEATScorer:
"""E-E-A-T 综合评分"""
@staticmethod
def score(b: BrandSignals) -> dict:
scores = {}
# === Experience (25分) ===
exp = 0
exp += min(b.case_studies * 3, 12)
exp += min(b.original_research * 4, 8)
exp += min(b.product_reviews_with_photos * 2, 5)
scores["Experience (经验)"] = min(exp, 25)
# === Expertise (25分) ===
ex = 0
ex += 8 if b.author_bios_complete else 0
ex += min(len(b.certifications) * 3, 12)
ex += min(b.expert_contributors * 2, 5)
scores["Expertise (专业性)"] = min(ex, 25)
# === Authoritativeness (25分) ===
auth = 0
auth += min(b.referring_domains // 20, 8)
auth += min(b.media_mentions * 2, 10)
auth += 4 if b.wikipedia_mentions else 0
auth += min(b.industry_awards * 2, 3)
scores["Authoritativeness (权威性)"] = min(auth, 25)
# === Trustworthiness (25分) ===
trust = 0
trust += 5 if b.https_enabled else 0
trust += 3 if b.privacy_policy else 0
trust += 2 if b.contact_page else 0
if b.reviews_avg >= 4.5:
trust += 10
elif b.reviews_avg >= 4.0:
trust += 7
elif b.reviews_avg >= 3.5:
trust += 4
trust += min(b.reviews_count // 50, 5)
scores["Trustworthiness (可信度)"] = min(trust, 25)
total = sum(scores.values())
level = (
"权威站点 🏆" if total >= 80 else
"成长站点 📈" if total >= 55 else
"初级站点 🌱" if total >= 30 else
"待建设 ⚠️"
)
return {
"总分": f"{total}/100",
"权威等级": level,
"分项得分": scores,
}
@staticmethod
def improvement_roadmap(scores: dict) -> list[str]:
"""改进路线图（按 ROI 排序）"""
tips = []
s = scores["分项得分"]
if s["Trustworthiness (可信度)"] < 18:
tips.append("🔴 短期: 完善 HTTPS、隐私政策、联系页面（1天内可完成）")
if s["Expertise (专业性)"] < 15:
tips.append("🔴 短期: 为每篇文章添加完整作者简介，包含实名+资质+社交链接")
if s["Experience (经验)"] < 12:
tips.append("🟡 中期: 发布 3–5 篇带真实数据的案例研究（含前后对比截图）")
if s["Authoritativeness (权威性)"] < 12:
tips.append("🟡 中期: 主动联系行业媒体，争取 3–5 个 Do-Follow 外链")
return tips or ["✅ E-E-A-T 基础完善，持续积累权威信号"]
# 演示 — 某电商 SEO 博客评估
brand = BrandSignals(
case_studies=4,
original_research=1,
product_reviews_with_photos=12,
author_bios_complete=True,
certifications=["Google Analytics", "HubSpot Content"],
expert_contributors=2,
referring_domains=180,
media_mentions=8,
wikipedia_mentions=False,
industry_awards=1,
https_enabled=True,
privacy_policy=True,
contact_page=True,
reviews_avg=4.3,
reviews_count=320,
)
scorer = EEATScorer()
result = scorer.score(brand)
print("=== E-E-A-T 品牌权威评估 ===")
print(f"  综合评分: {result['总分']}  {result['权威等级']}")
print("\n分项得分 (各 25 分):")
for dim, score in result["分项得分"].items():
bar = "█" * score + "░" * (25 - score)
print(f"  {dim[:20]:20s}: {score:2d}/25  {bar}")
print("\n改进路线图:")
for tip in scorer.improvement_roadmap(result):
print(f"  {tip}")

E-E-A-T 提升策略

行动清单

• [ ] 审查网站所有文章：是否有作者署名 + 完整作者简介（含真实资质和联系方式）
• [ ] 在品牌最重要的 5 篇文章中，补充原创数据、调研结果或真实案例截图
• [ ] 注册 HARO（Help A Reporter Out）或国内媒体资源，每周回复 3 条相关问题获得引用
• [ ] 建立品牌 Wikipedia 草稿（需满足 notability 标准），或确保品牌在权威目录中有收录
• [ ] 在 Google Business、Trustpilot 或行业平台收集 50+ 真实评价，维护平均 4.0 分以上
• [ ] 查看竞争对手的媒体引用来源（用 Ahrefs "Link Intersect"），优先争取同类媒体外链

下一节：03-竞争对手外链分析与反向工程 — 逆向拆解竞争对手的外链策略，找到可复制的获链路径。