Abstract: 【Purpose/significance】Aiming at the current situation of loose structure of online medical information and insufficient preci?
sion of online physician recommendation, a physician recommendation algorithm based on label and patient consultation text is de?
signed to improve the effect【. Method/process】Word2vec model is used to train patient consultation texts to obtain feature vectors and improve cosine similarity algorithm to calculate physician recommendation set A; LDA model is used to train labels to obtain probabil? ity distribution of projection on the subject and KL distance algorithm is improved to calculate set B; based on social network analysis theory,relevant algorithms are designed to reconstruct physician network links and select centrality index to obtain final set C【. Result/conclusion】Based on the relevant data of "doctor clove",the non-traditional UGC data enriches the availability of the algorithm,makes up for the deficiency of single recommendation method,and improves the accuracy.The proposed method effectively improves the accu? racy of doctor recommendation【. Innovation/limitation】By fusing label and patient consultation text,social network analysis is used to realize physician hybrid recommendation.Though the central indicators of important doctors mining,the effect has room to improve.