Reform of educational management work in higher education based on departmental sociology in the context of intelligent technology

Abstract

Abstract Based on Apriori algorithm and BP neural network technology, this paper deeply integrates association rules and data mining technology in education and informatization management, which provides a new method and a new way of thinking for reforming education management work in the context of intelligent technology. In real-life settings, education management is being managed using this new method. Taking the university district of DN201 as the research object, the allocation level of basic education resources in the university district is measured and optimized in a balanced way through indicator analysis. The parameter settings described allowed for the search of strong correlation rules for the mastery level of students in each course cluster. The results show that SN201 has the highest level of educational resource allocation at 8.32. SN215 is at the lowest level at 2.62. This shows that there is an imbalance in its resource allocation. The coefficient of variation of educational resource allocation of SN217 is 0.83 before optimization, and after optimization, the coefficient of variation is 0.65, and the variation becomes lower. The courses have a higher level of enhanced strong association than 4.