Few-Shot Learning-Driven Optimal Allocation Model of University Human Resources Based on the Apriori Algorithm

Abstract

For universities, HR is the first element and the first resource to build university resources. Systematic construction and management of HR and effective development and utilization are the key factors to promote the development of universities. In this paper, the Apriori correlation rule mining algorithm is studied, the optimal allocation model of university HR based on the Apriori algorithm is studied, and the strategy of optimal allocation of university HR is discussed. DM can discover the talent patterns and laws within the organization, so as to find the talent model within the organization. The results show that the method proposed in this paper can be used to mine the correlation rules of the university HR data set, and good results can be achieved. The system can realize the standardization, safety, and paperless work of university personnel, which has changed the original manual work mode and greatly improved the work efficiency. The method in this paper can be used to determine a reasonable HR structure according to the development goals and plans of our school, establish a scientific talent evaluation mechanism, clarify the age structure of teachers, provide an important scientific method, and provide objective decision support for formulating talent demand planning, talent recruitment, and training.