In the field of modern steel metallurgy, the precise control of molten steel quality constitutes a critical bottleneck constraining the performance enhancement of high-end steel products. As a domestic steel plant endeavors to upgrade its product quality, the increasingly stringent requirements for molten steel cleanliness and compositional homogeneity have rendered traditional, experience-reliant control modes for alloying and argon blowing inadequate to meet practical demands. Confronted with challenges such as alloy composition fluctuations and argon flow mismatches during the refining process—issues that frequently result in unstable molten steel quality, product rework, and grade downgrading—this study develops an intelligent argon blowing control system integrated with industrial vision, data analytics, and metallurgical mechanisms. Leveraging a dynamic coordination mechanism, the system achieves the integrated regulation of key process parameters. Industrial experiments show that: by employing a machine learning approach that combines metallurgical mechanisms, back propagation (BP) neural networks, and the improved simplex method, the compositional compliance rate for Q195 steel increases to 98.1%, while alloy costs were reduced by 5.3 CNY/ton; The industrial vision-guided adaptive intelligent argon blowing control system reduces argon consumption per ton of steel by 9.9%, decreases the proportion of inclusions with a grade of CT3.5 or higher from 35% to 16%, and yields an annual comprehensive benefit of approximately 2.2832 million CNY.