Analysis and Optimization Scheme of the Effectiveness of Word2vec in Intelligent Recommendation of Traditional Chinese Medicine Folk Prescriptions

With the rapid development of science and technology and medical intelligence, personalized intelligent recommendation has been applied more and more widely in the medical field. For a patient, it is a very important link to find a suitable prescription, but in the face of many treatment programs and prescriptions, it is difficult for users to quickly obtain effective information and make a choice suitable for their condition. By analyzing the individual needs of users, valuable information is mined from a large number of traditional Chinese medicine treatments and remedies, and it is recommended to users to provide help for users to get treatment in time. However, most of the existing TCM prescription recommendation systems are based on traditional collaborative filtering or content-based recommendation algorithms, which lack in-depth mining of the semantic information of TCM prescription. Therefore, this paper proposes an intelligent recommendation method for traditional Chinese medicine prescriptions based on Word2vec model, aiming to improve the accuracy and personalization of the recommendation system by learning the semantic representation of traditional Chinese medicine prescriptions. Since Word2vec model does not distinguish the semantic relationship between context words and central words, the semantic is relatively missing, and the effect on subsequent tasks is limited. Therefore, it is of great significance to propose an optimization plan to effectively promote the modernization and intelligent development of traditional Chinese medicine.