摘要：With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials *** learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data *** with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel *** review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model ***,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and *** application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also ***,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.