Data-Driven Segmentation and Targeting in China’s Mineral Resource Supply Chain: A Quantitative Study of Industry Buyers and Traders

China's mineral resource supply chain is crucial in global industrial production and economic stability. That is why political instability, environmental issues, and unstable materials prices make it compulsory for firms to employ data-driven segmentation and targeting techniques. This paper uses a quantitative approach to analyze big data, machine learning and advanced analytics for effective supply chain management in mineral resources. The study reveals that the industry buyers and traders can be classified depending on their purchasing activeness and willingness to take risks to design strategies that increase efficiency among the suppliers. It also looks into the roles of advanced frontiers like artificial intelligence and blockchain in enhancing transparency, demand forecasting and risk management. Also, international relations such as trade wars and interrupted supply chains impact China’s strategic management of mineral resources. The study provides policy prescriptions for increasing domestic production, engaging in international cooperation, and developing technology to reduce supply chain risks. Ultimately, data-driven segmentation and targeting offer a robust framework for strengthening China's position in the global mineral market while ensuring sustainability and security.