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Abstract With the accelerated advancements in artificial intelligence and the increasing emphasis on sustainable supply chain management, the integration of multimodal artificial intelligence (AI) into green supply chains has emerged as a critical research frontier. This study delves into the synergistic potential and challenges of combining multimodal AI, which leverages diverse data types such as text, images, and numerical data, to enhance decision-making processes in green supply chains. Through the meticulous design of a data strategy and model framework, this research establishes a sophisticated and efficient data processing and model training pipeline. The experimental results reveal that the comprehensive analysis and fusion of multimodal data significantly improve the prediction accuracy of key supply chain metrics, with observed increases in accuracy and recall rates by 12.4% and 9.8%, respectively. Additionally, the model's limitations are critically assessed, and targeted improvement strategies are proposed. The practical implications of this study are profound, offering actionable insights for the application of multimodal AI in real-world energy sector scenarios. The findings underscore the potential of this technology to optimize operations, reduce environmental impact, and drive sustainable growth in the energy industry. |
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