东莞公司建站模板网站seo诊断分析和优化方案

上一篇：1. 如何爬取自己的CSDN博客文章列表（获取列表）（博客列表）（手动+python代码方式）

写在前面

上一篇文章中，我们已经成功获取到了自己的CSDN已发布博文列表：

（articles.json）

本篇文章将实现获取每篇原创文章的质量分，并由小到大排序。

步骤

打开CSDN质量分页面

https://www.csdn.net/qc?utm_source=1966961068

粘贴查询文章url

按F12打开调试工具，点击Network，点击清空按钮

点击查询

是调了这个接口https://bizapi.csdn.net/trends/api/v1/get-article-score

用postman测试调用这个接口（不行，认证不通过）

POST https://bizapi.csdn.net/trends/api/v1/get-article-score

{"url": "https: //dontla.blog.csdn.net/article/details/132227398"
}

提示：

{"message": "X-Ca-Key is not exist"
}

然后我把X-Ca-Key从浏览器复制下来，给它加到Headers参数里了：

然后它又提示什么：

{"message": "X-Ca-Signature not exist"
}

然后我故技重施，把那些提示缺少的东西统统从浏览器复制下来给它加上：

但是最后提示：

{"message": "HMAC signature does not match"
}

这有点尴尬啊。。。

我查了一下，这种认证方式貌似是阿里云的API认证

有亿点复杂，一时半会搞不懂

这里有一篇巨好的参考文章

如何批量查询自己的CSDN博客质量分

参考上面参考文章中的获取质量分java代码部分，用python代码实现获取博文质量分（可以成功查询）

就是这一段：

// //循环调用csdn接口查询所有的博客质量分
String urlScore = “https://bizapi.csdn.net/trends/api/v1/get-article-score”;
//
//请求头
HttpHeaders headers = new HttpHeaders();
headers.set(“accept”,“application/json, text/plain, /”);
headers.set(“x-ca-key”,“203930474”);
headers.set(“x-ca-nonce”,“22cd11a0-760a-45c1-8089-14e53123a852”);
headers.set(“x-ca-signature”,“RaEczPkQ22Ep/k9/AI737gCtn8qX67CV/uGdhQiPIdQ=”);
headers.set(“x-ca-signature-headers”,“x-ca-key,x-ca-nonce”);
headers.set(“x-ca-signed-content-type”,“multipart/form-data”);
headers.setContentType(MediaType.MULTIPART_FORM_DATA);
//调用接口获取数据
List scoreModels = new ArrayList<>();
for (String bkUrl : urlList) {
MultiValueMap<String,String> requestBody = new LinkedMultiValueMap<>();
requestBody.put(“url”, Collections.singletonList(bkUrl));
HttpEntity<MultiValueMap<String, String>> requestEntity = new HttpEntity<>(requestBody, headers);
URI uri = URI.create(urlScore);
ResponseEntity responseEntity = restTemplate.postForEntity(uri, requestEntity, String.class);
JSONObject data1 = JSON.parseObject(responseEntity.getBody(),JSONObject.class) ;
ScoreModel scoreModel = JSONObject.parseObject(data1.get(“data”).toString(),ScoreModel.class);
scoreModels.add(scoreModel);
System.out.println("名称: "+scoreModel.getTitle() +"分数: " + scoreModel.getScore() +"时间: " + scoreModel.getPost_time());
}
return scoreModels;
}

传入参数为urlList：

import requests
from requests.models import PreparedRequestdef get_score_models(url_list):url_score = "https://bizapi.csdn.net/trends/api/v1/get-article-score"headers = {"accept": "application/json, text/plain, */*","x-ca-key": "203930474","x-ca-nonce": "22cd11a0-760a-45c1-8089-14e53123a852","x-ca-signature": "RaEczPkQ22Ep/k9/AI737gCtn8qX67CV/uGdhQiPIdQ=","x-ca-signature-headers": "x-ca-key,x-ca-nonce","x-ca-signed-content-type": "multipart/form-data"}score_models = []for bk_url in url_list:data = {"url": [bk_url]}response = send_request(url_score, data, headers)data1 = response.json()print(data1)'''{'code': 200, 'message': 'success', 'data': {'article_id': '132240693', 'score': 95, 'message': '文章质量良好', 'post_time': '2023-08-12 17: 45: 24'}}        '''score_model = data1["data"]score_models.append(score_model)print(f'文章Id：{score_model["article_id"]}\n分数：{score_model["score"]}\n文章质量：{score_model["message"]}\n发布时间：{score_model["post_time"]}')return score_modelsdef send_request(url, data, headers):session = requests.Session()prepared_request = PreparedRequest()prepared_request.prepare(method='POST', url=url,headers=headers, data=data)return session.send(prepared_request)# 示例调用
urlList = ["https://dontla.blog.csdn.net/article/details/132240693"]
scoreModels = get_score_models(urlList)

上面的验证信息，我从那篇博客里搞来的，怎么生成的，我就搞不清楚了。。。

运行上面代码，能成功得到质量分信息：

读取我们上一篇文章中的博客列表articles.json，逐个获取质量分，最后把结果保存到processed_articles.json★★★

我们上一篇文章得到的articles.json是这样的：

下面代码将读取它并逐个获取质量分：

（getArticleScore.py）

import requests
from requests.models import PreparedRequest
import jsondef get_score_models(url):url_score = "https://bizapi.csdn.net/trends/api/v1/get-article-score"headers = {"accept": "application/json, text/plain, */*","x-ca-key": "203930474","x-ca-nonce": "22cd11a0-760a-45c1-8089-14e53123a852","x-ca-signature": "RaEczPkQ22Ep/k9/AI737gCtn8qX67CV/uGdhQiPIdQ=","x-ca-signature-headers": "x-ca-key,x-ca-nonce","x-ca-signed-content-type": "multipart/form-data"}data = {"url": url}response = send_request(url_score, data, headers)data1 = response.json()# print(data1)'''{'code': 200, 'message': 'success', 'data': {'article_id': '132240693', 'score': 95, 'message': '文章质量良好', 'post_time': '2023-08-12 17: 45: 24'}}        '''score_model = data1["data"]return score_modeldef send_request(url, data, headers):session = requests.Session()prepared_request = PreparedRequest()prepared_request.prepare(method='POST', url=url,headers=headers, data=data)return session.send(prepared_request)def process_article_json():# 读取articles.json文件with open('articles.json', 'r') as f:articles = json.load(f)# 遍历每个元素并处理for article in articles:score_model = get_score_models(article['article_url'])article['article_score'] = score_model['score']print(article)# 保存处理后的结果到新的JSON文件output_file = 'processed_articles.json'with open(output_file, 'w') as f:json.dump(articles, f, ensure_ascii=False, indent=4)if __name__ == '__main__':process_article_json()

最终得到processed_articles.json：

编写代码处理processed_articles.json，提取原创文章，根据url去重，并按质量分由小到大排序，生成original_sorted_articles.json★★★

（getOriginalSort.py）

import json# 读取JSON文件
with open('processed_articles.json', 'r') as f:data = json.load(f)# 过滤和排序数据，并去除重复的元素
filtered_data = []
seen_urls = set()
for article in data:if article['article_type'] == '原创' and article['article_url'] not in seen_urls:filtered_data.append(article)seen_urls.add(article['article_url'])sorted_data = sorted(filtered_data, key=lambda x: x['article_score'])# 保存到新的JSON文件
with open('original_sorted_articles.json', 'w') as f:json.dump(sorted_data, f, indent=4, ensure_ascii=False)

执行：

python3 getOriginalSort.py

生成文件original_sorted_articles.json：

不看不知道，一看吓一跳啊，居然这么多一分的。。。心塞

编写代码统计original_sorted_articles.json中原创文章数量，计算平均质量分★★★

（getAverageScore.py）

import json# 读取 JSON 文件
with open('original_sorted_articles.json', 'r') as file:articles = json.load(file)# 统计 article_score 并计算平均值
total_score = 0
num_articles = len(articles)
for article in articles:total_score += article['article_score']
average_score = total_score / num_articles# 打印结果
print(f"元素数量：{num_articles}")
print(f"平均 article_score：{average_score}")# 保存结果到文本文件
with open('average_score_result.txt', 'w') as file:file.write(f"元素数量：{num_articles}\n")file.write(f"平均 article_score：{average_score}\n")

我去，这也太低了吧，客服咋给我算出60几分的，难道只统计最近一两年的？

唉，反正慢慢改吧。。。😔

搞了个监控程序，如果我们更新了博客，就去original_sorted_articles.json把对应的score置零，然后程序马上感应到并重新获取质量分，重新计算平均质量分★★★

（update_score.py）

import time
import json
import requests
from requests.models import PreparedRequestdef get_score_models(url):url_score = "https://bizapi.csdn.net/trends/api/v1/get-article-score"headers = {"accept": "application/json, text/plain, */*","x-ca-key": "203930474","x-ca-nonce": "22cd11a0-760a-45c1-8089-14e53123a852","x-ca-signature": "RaEczPkQ22Ep/k9/AI737gCtn8qX67CV/uGdhQiPIdQ=","x-ca-signature-headers": "x-ca-key,x-ca-nonce","x-ca-signed-content-type": "multipart/form-data"}data = {"url": url}response = send_request(url_score, data, headers)data1 = response.json()# print(data1)'''{'code': 200, 'message': 'success', 'data': {'article_id': '132240693', 'score': 95, 'message': '文章质量良好', 'post_time': '2023-08-12 17: 45: 24'}}        '''score_model = data1["data"]return score_modeldef send_request(url, data, headers):session = requests.Session()prepared_request = PreparedRequest()prepared_request.prepare(method='POST', url=url,headers=headers, data=data)return session.send(prepared_request)def getAverageScore(articles):# 统计 article_score 并计算平均值total_score = 0num_articles = len(articles)for article in articles:total_score += article['article_score']average_score = total_score / num_articles# 打印结果print(f"元素数量：{num_articles}")print(f"平均 article_score：{average_score}")def update_article_scores(file_path):while True:with open(file_path, 'r') as f:articles = json.load(f)for article in articles:if article['article_score'] == 0:print(f'监测到文章 {article["article_url"]} 改变，重新获取质量分')article['article_score'] = get_score_models(article['article_url'])['score']print(f'文章 {article["article_url"]} 新质量分为 {article["article_score"]}')# 排序# articles = sorted(articles, key=lambda x: x['article_score'])# 统计 article_score 并计算平均值getAverageScore(articles)with open(file_path, 'w') as f:json.dump(articles, f, indent=4, ensure_ascii=False,)print()time.sleep(1)  # 暂停1秒后再次遍历文件if __name__ == '__main__':# 在主程序中调用update_article_scores函数来更新article_scorefile_path = 'original_sorted_articles.json'update_article_scores(file_path)

（original_sorted_articles.json）

如果我们改了博客，把那篇的article_score置零：

这样实时计算就比较方便