This study focuses on the current situation that the public's awareness of food and drug safety has significantly increased, but drug information is complicated and difficult to find. Aiming at the dilemma of drug recommendation and information extraction, an intelligent system based on the Django framework is designed. The system uses natural language processing technology to parse the user's condition description, combines collaborative filtering algorithm to implement personalized drug recommendations, and introduces a dynamic weighted hybrid model to optimize the recommendation logic to ensure the accuracy of the recommendation. Finally, the recommendation results are presented through visualization to help users efficiently screen suitable drugs. Compared with traditional recommendation systems, the solution proposed in this study shows higher personalization and medical recommendation accuracy, providing strong support for the public's safe use of drugs.

Yang Qing, Gu Tianyu. Design of Drug Information Visualization and Intelligent Recommendation Platform. Academic Journal of Engineering and Technology Science (2025), Vol. 8, Issue 3: 79-87. https://doi.org/10.25236/AJETS.2025.080312.

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