This paper systematically discusses the integrated application path of natural language processing (NLP) and knowledge graph (KG) in medical text mining and knowledge extraction. When unstructured text data such as electronic medical records and clinical records emerge rapidly, traditional text processing methods are unable to meet the needs of medical information understanding and reasoning. NLP can improve the depth of semantic understanding, while KG can provide support for structured knowledge. The combination of the two can achieve semantic enhancement, entity standardization and clinical reasoning. The article accurately analyzes the processing challenges caused by the professionalism, ambiguity and heterogeneity of medical texts, and also reviews pre-trained models such as BioBERT and MedGPT and their performance in medical entity recognition and relationship extraction. The author introduces a system architecture solution that integrates the "extraction-standardization-fusion-reasoning" process, including the establishment of multi-level knowledge graphs, GNN reasoning principles and cross-platform deployment methods. At the end of the article, three application scenarios, namely auxiliary diagnosis, clinical decision support and public health monitoring, are used to illustrate the effectiveness of the system in actual medical environments. This study has built a theoretical and practical foundation for the advancement of intelligent medical knowledge computing.

Chun Chen. Integrated Application of Natural Language Processing and Knowledge Graph in Medical Text Mining and Knowledge Extraction. International Journal of World Medicine (2025), Vol. 6, Issue 1: 34-44. https://doi.org/10.38007/IJWM.2025.060105.

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