Intelligent matching algorithm for application cases of higher vocational medical microbiology laboratory courses

Abstract

In the context of digital education, efficient management and intelligent application of teaching resources for higher vocational medical microbiology experimental courses are crucial to improving teaching quality. Currently, platforms supporting these courses often rely on rudimentary matching that fails to mine deep semantic associations between cases or accurately identify similarities in core elements, leading to low matching efficiency. This research proposed an intelligent matching algorithm for application cases in higher vocational medical microbiology laboratory courses. Centered on a structured semantic model, the algorithm employed an "entity-relationship-entity" framework for multi-dimensional case analysis. A case library was constructed through processes including data collection and cleaning, knowledge graph mapping, and semantic enhancement. Targeting the characteristics of long-text case descriptions, a method integrating text summarization extraction and a relevance evaluation mechanism was introduced. Supervised datasets were built by annotating the relevance of text fragments based on core course elements, enabling iterative optimization of the evaluation mechanism for calculating precise case feature weights. For a target case, an information table was constructed, and attribute weights were determined using the knowledge granularity rough set principle combined with expert experience. Similarity was calculated via Euclidean distance measurement and subsequently converted into a similarity score. Eventually, a weighted average algorithm comprehensively evaluated similarity across multiple fields, and matching rules were formulated to achieve intelligent case matching. The results demonstrated that the proposed method performed excellently in both matching degree and speed, effectively improving overall matching efficiency. This research provided a robust technical framework for case-based teaching in vocational medical education and offered significant value to the scientific teaching community by enhancing the precision and efficiency of resource retrieval and recommendation in specialized courses.