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While nursing courses provide a convenient and quick way to learn, they can also be overloaded with resources that can cause learners to become cognitively disoriented or have difficulty choosing nursing course. This paper proposes to fully explore learners’ interests in the case of sparse data by fusing knowledge graph technology and deep recommendation models and adopt knowledge graph to model nursing courses at the semantic level so as to correspond the set of nursing courses to the knowledge graph and solve the problem of lack of logical knowledge relationships. Due to the specificity of its positions, the nursing profession must accurately position the nursing professional curriculum standards in the process of determining the talent cultivation model based on the nursing professional positions and the admission requirements for nursing practice qualification. Through linear feature mining based on the knowledge graph, entities and relationships are used to intuitively display the interest paths of nursing professional learners and enhance the interpretability of recommendations.

Research on a Health Care Personnel Training Model Based on Multilayered Knowledge Mapping for the Integration of Nursing Courses and Examinations