Research and Implementation of QC Knowledge Management Model for Tobacco Enterprises Based on AI
Year: 2025
Type: conference-paper
Abstract: To address issues in tobacco industry QC knowledge management-decentralized resources, inefficient retrieval, and repetitive labor—an intelligent framework using DeepSeek large model is built. By integrating the QC knowledge base with DeepSeek-R1 engine, it enables intelligent extraction, multi-dimensional semantic storage, and precise retrieval. Experiments show inputting natural language yields a 92.3% relevant QC knowledge graph, boosting reuse efficiency and retrieval accuracy, supporting digital transformation of tobacco QC knowledge management.
Author: Xiaohan Sun
Institution: China Tobacco
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414396141