基于大模型深度语义理解的智能内容纠错系统

Intelligent Content Correction System Based on Deep Semantic Understanding of Large Language Models

摘要: 针对传统网页内容纠错效率低下、语义理解能力不足，以及现有方法难以兼顾大规模数据采集与深度语义分析的问题，设计并实现了一种创新的、端到端的自动网页语义纠错报告系统。该系统有效整合了现有网络爬虫、分布式任务队列、多线程并发以及大语言模型的深度语义推理技术，解决了网页内容自动化语义级纠错这一全新复杂应用问题，实现了从网页数据采集到错误报告生成的完整闭环流程。通过模块化“子处理器”设计，支持插件化扩展与多模态输入；利用任务队列与线程池协同，缓解爬虫高速抓取与模型推理的速度差异。该系统目前主要针对特定新闻类网页结构设计，可快速扩展至其他站点。研究成果填补了传统纠错技术在语义层面的空白，为内容安全、企业效率及数字经济中的智能纠错应用提供了可行框架。

Abstract: To address the low efficiency and insufficient semantic understanding in traditional web content correction, as well as the challenge that existing methods face in balancing large-scale data collection with deep semantic analysis, this paper designs and implements an innovative end-to-end automatic web semantic error correction reporting system. By effectively integrating existing technologies—including web crawlers, distributed task queues, multi-threaded concurrency, and the deep semantic reasoning capabilities of Large Language Models (LLMs)—the system successfully solves the entirely new and complex application problem of automated semantic-level error correction for web content. It realizes a complete closed-loop workflow from web data acquisition and semantic analysis to error report generation. Through a modular “sub-processor” design, the system supports plug-in expansion and multi-modal input; meanwhile, the coordination between task queues and thread pools effectively alleviates the speed disparity between high-speed crawling and model inference. Although currently tailored primarily to specific news website structures, the system can be rapidly extended to other sites. The research outcomes bridge the semantic gap in traditional correction technologies and provide a viable framework for intelligent correction applications in content security, enterprise efficiency, and the digital economy.