Artificial Intelligence Technology Facilitates Model Innovation in Higher Education Management and Student Training Mechanisms

Abstract

Abstract In order to promote the model innovation of higher education management and student training mechanisms, this paper proposes data mining technology to assist the research of education management and training mechanisms. Using web crawler technology to obtain the required initial data for the study, data cleaning and conversion are carried out to ensure the accuracy of the results of the research and analysis in response to the problems of missing data, intermingled noise, and attribute redundancy in the initial data. Aiming at the slow efficiency and collapse of the traditional association rule algorithm in the face of massive data, the whale optimization algorithm is used to optimize the association rule algorithm for multi-objective optimization, and the optimized association rule algorithm is used to analyze the data of higher education management. The data showed that the Confidence=77.82% for the association rule poor grades in introductory courses=>poor grades in major courses, which indicates that the probability of students whose average grades in introductory courses are outside the 75th percentile and those whose average grades in significant classes are outside the 60.00th percentile is 77.82%. This study aids teachers and administrators in making scientific decisions to enhance teaching management, enhance teaching quality, and reduce costs associated with school operations.