Building on our company's practical experience, we present Intelligent Site-Wide Profiling and Adaptive Crawling Technology: A two-stage crawling architecture based on large language models, achieving automatic website type recognition, intelligent content modality discrimination, and differentiated strategy routing. Compared to traditional methods, accuracy improves by 25-40%, maintenance costs reduce by 60-80%, supporting intelligent recognition of 10 website types and 7 content modalities.
Based on our company’s years of industry experience and technical implementation practices in public opinion monitoring and web data mining & analysis, this article explores the progression and paradigm shift of web parsing technologies from rule-driven to semantic understanding. It analyzes the revolutionary value and inherent limitations of LLMs for data extraction, and proposes a hybrid architecture solution.
In vast information fields like engineering investment and international trade, **accurately extracting core project elements** (e.g., project name, investment amount, executing agency) is crucial for corporate decision-making. Traditional methods rely on supervised learning, while the advent of large models is revolutionizing this domain. Based on real business scenarios undertaken by our company, data annotation costs, technical implementation paths, and case comparisons, this article provides a deep analysis of the differences and selection strategies between these two technical routes.