This study develops a knowledge graph-based intelligent Q&A system for rare diseases, integrating data from 126 diseases, 2,609 symptoms, and related departments. The Bert-BiLSTM-CRF model achieved 82.13% accuracy in entity extraction, and TextCNN achieved 94.54% accuracy in intent recognition. The system improves access to medical knowledge but requires further optimization to reduce response times and handle a broader range of user queries.