基于机器学习的Web网络爬虫算法优化研究
Research on Optimization of Web Crawler Algorithm Based on Machine Learning
刘俊培 1贾继洋 2班岚 1迟欢 1孙沛叶1
作者信息
- 1. 北京科技大学天津学院,天津 301800
- 2. 枣庄学院,山东枣庄 277000
- 折叠
摘要
随着互联网的不断发展,网络爬虫在信息获取和数据挖掘等领域中的应用越来越广泛.同时在互联网相关应用中,机器学习技术成为一种非常重要的手段,能够完成更加高效和准确的网络爬取.然而,现有的网络爬虫算法还存在着很多问题,比如效率低下、容易被封禁等.因此,本文对现有的网络爬虫算法进行分析和总结,找出其中存在的问题和不足之处,提出一种基于机器学习的网络爬虫算法优化方法,使其更加智能和自适应,以期更好地满足实际应用的需求.
Abstract
With the continuous development of the Internet,web crawlers are more and more widely used in the fields of information acquisition and data mining.At the same time,in Internet related applications,machine learning technology has become a very important means to achieve more efficient and accurate network crawling.However,existing web crawler algorithms still have many problems,such as low efficiency and easy to be banned.Therefore,this article analyzes and summarizes existing web crawler algorithms,identifies their problems and shortcomings,and proposes a machine learning based optimization method for web crawler algorithms to make them more intelligent and adaptive,in order to better meet the needs of practical applications.
关键词
机器学习/Web网络爬虫算法/算法优化Key words
machine learning/Web crawler algorithm/algorithm optimization引用本文复制引用
出版年
2024
NETL
NSTL
万方数据