Research on the construction and dynamic adaptation algorithm of cognitive graph multimodal knowledge network for enterprise management communication

Modern enterprise management faces data from many sources and many types. The data are split, so decision speed and strategic collaboration drop, especially in fast-changing situations like supply chain breaks. Existing methods like fuzzy cognitive maps (FCMs) are static, so they do not handle these cases well, and they have limits in joining multimodal data. This study gives a method to build multimodal management knowledge networks with cognitive maps and a dynamic update algorithm. The method has three steps: first, align multimodal features with an enhanced ELMo model; next, build a quantum embedding model to help cross-domain generalization; then, use an event-driven weight update for real-time change. In a supply chain test, the method raises decision-making efficiency by 74.4%, reaches 92.1% accuracy in predicting chip-shortage risk paths, and raises supplier turnover by 77.1%.