本地 SEO 决定企业是否出现在 Google 地图和地理定位查询的本地包中。本地 SEO 检查器会分析企业在本地结果中的显示情况：其评级、评论数量、是否拥有网站以及相对于竞争对手的排名如何。本教程使用 Scavio API 构建本地 SEO 检查器，查询目标关键字的本地结果，评估企业列表质量，并生成本地 SEO 得分报告。
前置条件
- Python 3.8 或更高版本
- 请求已安装库
- Scavio API 密钥
- 要检查的公司名称和地点
操作指南
步骤 1: 获取目标关键词的本地结果
搜索您的企业应排名的本地意图关键字。 local_results 字段包含 Google 地图列表。
Python
def get_local_results(keyword: str, location: str) -> list[dict]:
r = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"query": f"{keyword} in {location}", "country_code": "us"}
)
r.raise_for_status()
return r.json().get("local_results", [])步骤 2: 在结果中找到目标企业
扫描本地结果以查找目标企业名称。如果找到，则返回其位置和列表详细信息。
Python
def find_business(results: list[dict], business_name: str) -> dict | None:
for i, r in enumerate(results):
if business_name.lower() in r.get("title", "").lower():
return {"position": i + 1, **r}
return None步骤 3: 对列表质量进行评分
根据评级、评论计数、网站存在和电话号码可用性计算本地 SEO 分数。
Python
def score_listing(listing: dict) -> dict:
score = 0
factors = {}
rating = float(listing.get("rating", 0) or 0)
if rating >= 4.5:
score += 30
factors["rating"] = f"{rating}/5 (excellent)"
elif rating >= 4.0:
score += 20
factors["rating"] = f"{rating}/5 (good)"
else:
factors["rating"] = f"{rating}/5 (needs improvement)"
reviews = int(listing.get("reviews", 0) or 0)
if reviews >= 100:
score += 25
elif reviews >= 20:
score += 15
factors["reviews"] = str(reviews)
if listing.get("website"):
score += 20
factors["website"] = "present"
else:
factors["website"] = "missing"
if listing.get("phone"):
score += 15
factors["phone"] = "present"
else:
factors["phone"] = "missing"
return {"score": score, "max_score": 90, "factors": factors}步骤 4: 生成本地 SEO 报告
对多个关键字运行检查器并输出全面的本地 SEO 报告。
Python
def local_seo_check(business: str, location: str, keywords: list[str]) -> None:
print(f"Local SEO Report: {business} in {location}\n" + "=" * 50)
for kw in keywords:
results = get_local_results(kw, location)
match = find_business(results, business)
if match:
scores = score_listing(match)
print(f"\n{kw}: Position #{match['position']}, Score: {scores['score']}/{scores['max_score']}")
else:
print(f"\n{kw}: Not found in local results")Python 示例
Python
import os
import requests
API_KEY = os.environ.get("SCAVIO_API_KEY", "your_scavio_api_key")
ENDPOINT = "https://api.scavio.dev/api/v1/search"
def get_local(kw: str, loc: str) -> list[dict]:
r = requests.post(ENDPOINT, headers={"x-api-key": API_KEY},
json={"query": f"{kw} in {loc}", "country_code": "us"})
r.raise_for_status()
return r.json().get("local_results", [])
def score(listing: dict) -> int:
s = 0
if float(listing.get("rating") or 0) >= 4.0: s += 25
if int(listing.get("reviews") or 0) >= 50: s += 25
if listing.get("website"): s += 25
if listing.get("phone"): s += 15
return s
if __name__ == "__main__":
business = "Joe's Coffee"
keywords = ["coffee shop", "best coffee", "cafe near me"]
for kw in keywords:
results = get_local(kw, "Austin, TX")
match = next((r for r in results if business.lower() in r.get("title", "").lower()), None)
if match:
print(f"{kw}: #{results.index(match)+1}, score={score(match)}/90")
else:
print(f"{kw}: not found")JavaScript 示例
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY || "your_scavio_api_key";
const ENDPOINT = "https://api.scavio.dev/api/v1/search";
async function getLocal(kw, loc) {
const res = await fetch(ENDPOINT, {
method: "POST",
headers: { "x-api-key": API_KEY, "Content-Type": "application/json" },
body: JSON.stringify({ query: `${kw} in ${loc}`, country_code: "us" })
});
const data = await res.json();
return data.local_results || [];
}
async function main() {
const business = "Joe's Coffee";
const keywords = ["coffee shop", "best coffee"];
for (const kw of keywords) {
const results = await getLocal(kw, "Austin, TX");
const idx = results.findIndex(r => r.title?.toLowerCase().includes(business.toLowerCase()));
if (idx >= 0) {
console.log(`${kw}: #${idx + 1}, rating: ${results[idx].rating}`);
} else {
console.log(`${kw}: not found`);
}
}
}
main().catch(console.error);预期输出
JSON
Local SEO Report: Joe's Coffee in Austin, TX
==================================================
coffee shop: Position #3, Score: 75/90
rating: 4.7/5 (excellent)
reviews: 234
website: present
phone: present
best coffee: Position #5, Score: 65/90
cafe near me: Not found in local results