传统文本挖掘研究偏重实验室算法与理论，难以适配产业需求。本文立足产业化落地需求，搭建大模型文本挖掘系统，分析适配产业的主流技术，设计数据层、模型调度层、应用服务层三层架构，打通数据治理至业务服务全链路。实例证明该系统可降本增效，助力自然语言处理技术规模化商用，为各行业数字化转型提供可复用工程方案。

Traditional text mining research predominantly focuses on laboratory-oriented algorithms and theoretical frameworks, which hardly align with industrial practical demands. Centered on industrial implementation requirements, this paper constructs a large-model-based text mining system. It analyzes mainstream technologies applicable to industrial scenarios and designs a three-tier architecture consisting of a data layer, a model scheduling layer, and an application service layer, thereby realizing full-link integration from data governance to business services. Practical case studies verify that the proposed system can reduce costs and improve efficiency, facilitate the large-scale commercialization of natural language processing technologies, and deliver reusable engineering solutions for the digital transformation of various industries.