Knowledge Supply Chain Furnace: AI Cross-Modal Extraction Training Method for Business Administration Case Generation

Abstract

By constructing a knowledge supply chain model with both theoretical and practical value, this study proposes a novel approach to integrating multimodal data—such as text, financial reports, video cases, and business models—to generate teaching cases. The experiment employs a privatized Deepseek32b system, utilizing multimodal knowledge embedding technology, cognitive logic injection mechanisms, and systematic design of a teaching logic enhancer to significantly improve interdisciplinary knowledge integration and extraction efficiency. The experimental results show that generative artificial intelligence consistently produces an excess of teaching cases, with a significantly higher coverage of knowledge points compared to traditional NLP and manual methods. While generative AI exhibits stable logical coherence, its content logic is slightly inferior to that of high-quality human-generated works. This study verifies the effectiveness of the cross-modal knowledge extraction training method and provides valuable reference insights.