The fragmentation of knowledge in the pharmaceutical field, insufficient retrieval efficiency, and deviation in personalized services have long constrained the effective dissemination and clinical application of professional information. Although conventional medical recommendation systems can meet basic service needs, such systems face two major bottlenecks when integrating complex knowledge graphs in the pharmaceutical field: firstly, it is difficult to fully parse deep semantic associations in the knowledge network; Secondly, it is impossible to accurately track the dynamic evolution characteristics of user needs. In view of this, this study explores the integration of pharmaceutical knowledge resources and the improvement of recommendation accuracy from two dimensions, focusing on innovative research on efficient pharmaceutical knowledge graph construction technology and intelligent recommendation algorithm optimization based on semantic networks. The aim is to break through the technical constraints of traditional recommendation systems and build more explanatory and adaptive knowledge service solutions for the medical industry.

Qian Tan. Knowledge Graph Enhanced DRL-based Personalized Medication Recommendation System. International Journal of Health and Pharmaceutical Medicine (2025), Vol. 5, Issue 1: 11-21. https://doi.org/10.38007/IJHPM.2025.050102.

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