Research on Optimization Strategies of Artificial Intelligence Algorithms for the Integration and Dissemination of Pharmaceutical Science Popularization Knowledge

Abstract

With the rapid development of artificial intelligence technology, its application prospects in the field of medicine and health have attracted much attention, especially in the integration and dissemination of pharmaceutical science knowledge, where it has shown enormous potential. This study focuses on optimizing strategies for the dissemination of pharmaceutical popular science knowledge using artificial intelligence algorithms, proposing the use of advanced deep learning techniques to cluster, classify, and analyze patterns in large-scale pharmaceutical data, achieving personalized customization and efficient dissemination of popular science content. The study employs convolutional neural networks (CNN) for feature learning, leverages natural language processing (NLP) technology to optimize information retrieval and data management capabilities, and improves the fluency and relevancy of text generation with recurrent neural networks (RNN).  The research has achieved significant results in areas such as the automatic identification of drug molecular structures, the interpretation of drug action mechanism maps, and the generation of health guidance content. Comparative experiments show that artificial intelligence algorithms integrating deep learning and natural language processing are more accurate in understanding user needs and providing customized information responses compared to traditional methods. There is also a marked improvement in the rate of knowledge dissemination and the user interaction experience. However, the research also points out the challenges faced by the technology in ensuring information accuracy, dealing with ethical and privacy concerns, and handling cultural differences in cross-cultural communication. This study not only expands the application scope of artificial intelligence in the field of pharmaceutical popular science but also provides strategic reference for optimizing knowledge dissemination methods and promoting safe public use of drugs.