Research on the Application of Natural Language Processing Technology in Legal Case Teaching and Intelligent Question-Answering

Legal education plays a crucial role in cultivating analytical thinking and practical skills in the legal field. However, students often encounter difficulties in understanding legal provisions, analyzing cases, and grasping the logic behind judicial decisions. Traditional legal education relies heavily on case studies and manual interpretation, which can be time-consuming and challenging due to the complexity of legal language and terminology. To enhance the efficiency of legal education, this study proposes an intelligent legal case analysis and question-answering system based on Natural Language Processing (NLP) techniques. This study employs a framework that integrates BERT for feature extraction and structured analysis of legal texts, along with GPT for generating natural language responses to legal inquiries. The system also leverages natural language generation to provide well-structured and contextually relevant legal answers, improving accessibility to legal knowledge. Experimental results indicate that the system achieves $91 \%$ accuracy in legal case analysis and question-answering tasks, with an F1-score of 0.87.