Unlocking DeepSeek: How to Use AI-Powered Python Scraping for Baidu Hot Topics

1. Introduction

DeepSeek is an AI platform developed by the Chinese company DeepSeek, focusing on natural language processing (NLP), large‑model deep learning, multimodal fusion, and industry applications such as finance, healthcare, education, and customer service.

2. Core Features of DeepSeek

NLP : excels in Chinese text processing and semantic understanding; supports tasks like text generation, sentiment analysis, QA, and machine translation.

Large‑Model & Deep Learning : built on large‑scale deep learning models, capable of handling complex language tasks through pre‑training and fine‑tuning.

Multimodal Fusion : not limited to text, also supports image and speech data integration.

Industry Applications : provides intelligent QA, knowledge‑graph construction, automated document processing, and more across various sectors.

Open Ecosystem : offers APIs and tools for developers and enterprises to integrate its technology quickly.

3. Main Products

Intelligent QA System : delivers fast, accurate answers for customer service, education, etc.

Text Generation & Summarization : automatically creates high‑quality content and provides summarization.

Knowledge‑Graph Construction : builds structured knowledge graphs from large text corpora.

Sentiment Analysis & Public Opinion Monitoring : detects sentiment trends and monitors real‑time public opinion.

4. Technical Advantages

Strong Chinese Language Capability : optimized for Chinese linguistic characteristics.

Efficiency & Scalability : supports large‑scale data processing and high‑concurrency scenarios.

Continuous Learning & Iteration : ongoing model optimization improves performance and user experience.

5. Practical Example: Scraping Baidu Hot Topics with Python

The author asked DeepSeek to generate Python code that fetches the titles of hot topics on Baidu’s homepage. The response uses the requests library to send HTTP requests and BeautifulSoup to parse HTML.

import requests
from bs4 import BeautifulSoup

def fetch_baidu_hot_topics():
    # Baidu homepage URL
    url = 'https://www.baidu.com'
    # Send HTTP GET request
    response = requests.get(url)
    # Check if request succeeded
    if response.status_code == 200:
        # Parse HTML with BeautifulSoup
        soup = BeautifulSoup(response.text, 'html.parser')
        # Locate the div containing hot topics (class 's-hotsearch-content')
        hot_topics_div = soup.find('div', class_='s-hotsearch-content')
        if hot_topics_div:
            # Extract all topic titles
            hot_topics = hot_topics_div.find_all('a')
            for topic in hot_topics:
                print(topic.get_text().strip())
        else:
            print("Hot topics not found")
    else:
        print(f"Request failed, status code: {response.status_code}")

if __name__ == "__main__":
    fetch_baidu_hot_topics()

6. Code Explanation

requests.get(url) : sends an HTTP GET request to Baidu.

soup = BeautifulSoup(response.text, 'html.parser') : parses the returned HTML.

soup.find('div', class_='s-hotsearch-content') : locates the container with hot topics.

hot_topics_div.find_all('a') : finds all anchor tags representing individual topics.

topic.get_text().strip() : extracts and cleans the topic title.

7. Running the Code

Ensure requests and beautifulsoup4 are installed: pip install requests beautifulsoup4 Save the script (e.g., fetch_baidu_hot_topics.py) and execute it with python fetch_baidu_hot_topics.py.

8. Notes

Baidu’s page structure may change; you may need to adjust the class name or tags accordingly.

The script is for learning and testing only; respect the target site’s terms of service and privacy policies.

Conclusion

DeepSeek offers powerful AI capabilities in NLP, multimodal processing, and industry solutions, and its open ecosystem enables developers to quickly build intelligent applications. The provided Python example demonstrates how to leverage these tools for practical web‑scraping tasks.