Scheduling constraints and implementation in teaching management systems incorporating association rule mining algorithms

This paper examines class schedules, precautions, and association rule algorithms and builds a more scientific class scheduling system. Data mining technology association rules handle scheduling conflicts. This method extracts efficient negative sequence rules from patterns. Using local utility value and utility confidence, e-HUNSR formalises the problem of efficient negative sequence rules, generates candidate rules and a pruning strategy quickly, designs a data structure to store the necessary information, and proposes an efficient way to compute the antecedent local utility value and a simplified utility value calculation. Association rules and mining are used to solve the scheduling problem. The system can conduct course queries, OSes, and performs well in data mining. After experimental verification, the hybrid method with different scheduling condition criteria obtains 98.12% course selection satisfaction. Rule satisfaction averages 94.98%, and intelligent scheduling system scheduling efficiency is 91.91%. Adding fresh ideas and methods to the intelligent scheduling system increases instructional resources and university scheduling. Smarter university timetable management allocates teaching resources and completes education and teaching plans.