The invention discloses a medical question-answering system construction method based on a language model and entity matching. The method comprises the steps: S1, data collection; S2, deep neural network model design; S3, named entity recognition model training and knowledge graph construction; S4, complete medical retrieval type question-answering system construction. The method specifically comprises: collecting network medical discussion posts, cleaning the network medical discussion posts, and storing the cleaned network medical discussion posts into ElasticSearch to serve as a retrieval data set; processing open source data of the competition data set by using a medical natural language, and training a named entity recognition model related to medical treatment; and collecting a public data set of the open source website to form a medical knowledge graph so as to expand a retrieval process. According to the medical question-answering system method based on language model and entity matching, after the question-answering system is constructed and recalled, finely arranged and comprehensively scored, the most appropriate answer is output in combination with a reasonable scoring mechanism, and the defects of a retrieval type question-answering system and a knowledge graph type question-answering system are overcome.