With great application potential in the iron and steel industry, the industrial digital twin technology in the iron and steel industry becomes a core technology for the digital transformation of the plant, especially in terms of data modeling. The work aims to review the application of data modeling technology in industrial digital twin in the iron and steel industry. The paper focused on analyzing four data modeling methods based on literature research:knowledge-based method, mechanism-based method, traditional machine learning method and deep learning method, and introduced the advantages, limitations and specific application cases of these methods in detail. The fusion potential of these methods in the construction of industrial digital twins was discussed. In addition, it also took into consideration the scalable design of future models, especially for the application of large model technologies. This review provides an in-depth understanding of existing industrial digital twin data modeling technologies in the iron and steel industry, valuable insights into the digital transformation, and directions for future research and practice.