Medical terminology is characterized by strong heterogeneity and scarce labeled data, leading to weak semantic feature representation and low retrieval accuracy. To address these issues, a semi-supervised learning-based fuzzy retrieval algorithm for medical term information keywords is proposed. Word embeddings are utilized to standardize and map original medical term information. Based on core metadata information nodes and user query information nodes, the confidence distance between retrieval samples is determined, and a vector space model is employed to construct a metadata feature space for medical term information. Within this metadata feature space, data are merged according to their temporal attributes, thereby synchronizing data indexes.Semantic features are enhanced through multi-level index clustering and contextual encoding. The MixMatch semi-supervised learning algorithm, based on consistency regularization and entropy minimization principles, is introduced. A small amount of labeled data is used for data augmentation of unlabeled data, generating low-entropy pseudo-labels for unlabeled data to enhance semantic feature representation. The similarity between retrieval terms and candidate information is then calculated, enabling efficient matching across categories or synonymous terms. Experimental results demonstrate that applying the proposed method for medical term information keyword retrieval yields a retrieval result relevance exceeding 95% , effectively improving information retrieval accuracy.

semi-supervised learning,  medical terminology, path, information keywords, fuzzy retrieval