Abstract: The steelmaking–continuous casting section is a critical part that determines the quality of steel products. It highlights the characteristics of matter state transformation, matter property control, and mass flow management during steel production. The production process is highly complex and involves intricate physical and chemical reactions, as well as heat and mass transfer processes between multiple components. Additionally, there is a coupling of the temperature field and flow field within the high-temperature molten metal reaction vessel. The process is also marked by high dynamics, nonlinearity, and significant uncertainty. The regulatory mechanism is not well understood and is easily influenced by factors such as raw material composition and process operation, which leads to complex operational control and significant fluctuations in product quality and makes it difficult to achieve collaborative manufacturing with intelligent technologies in the production process. Therefore, it is urgent to break through key technologies such as data cognition and production decision-making in the steelmaking–continuous casting process. This would help promote high-quality, efficient, green, and low-carbon production while enhancing the digital and intelligent capabilities of the steel industry. According to the technological framework for collaborative manufacturing with intelligent technologies in the steelmaking–continuous casting section, this study explores various scales, including the unit device scale, workshop process scale, and steelmaking–continuous casting process scale. A comprehensive system level was established by integrating and synthesizing data levels, process device levels, process interface levels, and planning and scheduling levels. In-depth research was conducted on vertical and horizontal collaborative intelligent manufacturing across these different levels and processes. Systematic modeling and development were performed from process manufacturing at the process/device scale to the manufacturing at the steelmaking–continuous casting section. First, process control models for the converter, refining, and continuous casting processes were developed at the process/device level. Subsequently, a multi-process collaborative control model was constructed at the process linkage, production planning, and scheduling levels. The comprehensive integration and dynamic collaboration of manufacturing execution system (MES), process control, process operation control, and production planning and scheduling systems were achieved through the development of key process control models, production planning and scheduling models, and data interfaces between MES, dynamic knowledge graphs, and digital twins systems. Additionally, the autonomous evolution of cognitive knowledge graphs and the visualization of virtual space twins are successfully realized. This effort involves comprehensive modeling and development, which progress from the steelmaking–continuous casting section’s process/device level to the planning and scheduling level. Ultimately, it reaches the system integration level and encompasses both cognitive knowledge graphs and digital twin systems. The study enables collaborative operation control and dynamic decision-making for the steelmaking–continuous casting section by leveraging mechanistic models, data models, and expert knowledge synergistically, along with bidirectional interactions between virtual and real models and horizontal and vertical collaboration across multiple processes and levels. The results provide a significant reference value for the intelligent, green, and high-end development of the metallurgical industry. They also offer valuable insights for intelligent manufacturing in process industries and contribute substantially to advancing new productive forces in the steel industry and addressing critical challenges.