Direct reduced iron has a low impurity content and is a high-quality raw material for electric arc furnace steelmaking under the condition of shortage of steel scrap resources. Based on the material balance and energy balance theory, the paper constructs an intelligent metallurgical model of DRI electric arc furnace integrating production big data, and uses industrial production data to continuously optimize and calibrate the model. It analyzes from different dimensions the impact of DRI charging ratio, charging temperature, total iron content, metallization rate and carbon content on electric arc furnace smelting power consumption, lime consumption, carbon consumption, oxygen consumption, natural gas consumption, electrode consumption, steel scrap consumption, DRI consumption, smoke volume and smelting cost, providing technical reference and guidance for low-cost smelting in DRI electric arc furnaces. 

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