An intelligent legal education platform based on knowledge graph integration and machine learning-driven adaptive learning systems

With the increasing demand for practical ability and personalized learning in legal education, the traditional teaching mode faces many challenges such as lagging content update and insufficient teaching interactivity. This paper focuses on the structured expression of legal knowledge and the demand for intelligent teaching, and proposes the construction of an intelligent teaching platform for legal knowledge that integrates knowledge mapping and machine learning technology. Through automatic parsing and semantic modeling of legal texts, the platform constructs a high-quality legal knowledge graph, and combines natural language processing and personalized recommendation algorithms to realize intelligent reasoning, personalized learning path generation and automatic evaluation of legal knowledge. The system not only improves the intelligent level of legal teaching, but also promotes the synergistic development of students' legal thinking and practical ability. Through experimental validation and case study, this paper demonstrates the significant advantages of the platform in terms of teaching effect enhancement, precise pushing of learning resources and analysis of students' learning behavior. The research results show that the platform has good application prospects and promotion value in the intelligent transformation of legal education.