Abstract: Under the background of the“dual carbon”strategy implement and industry green transformation, the current teaching of polluted soil remediation technology courses is facing structural challenges. As a core professional course in environmental engineering, the course of polluted soil remediation technology involves multiple fields such as physics, chemistry, biology, and environmental engineering, and its knowledge system presents highly interdisciplinary characteristics. Based on this characteristic, in response to the problems of fragmented knowledge, disconnected practice, and dynamic industry demand in the course of polluted soil remediation technology, this article proposes to efficiently construct a knowledge graph of polluted soil remediation technology course using AI technology. Through intelligent analysis and identification of key knowledge and skill points, a structured knowledge system is formed to help students better understand and master the course content. The significant achievements of curriculum reform have verified the effectiveness of this model in enhancing engineering decision-making power and ecological mission, providing a paradigm for the digital transformation of new engineering education.

Key words: Knowledge graph, Environmental engineering, Integration of industry and education, Artificial intelligence, Personalized teaching, Efficient teaching