Abstract
This study combines intelligent resource optimization technology to build a dynamic student data management model and suggests a fuzzy hierarchical network representation model based on isomorphism and homogeneity in order to increase the effectiveness of dynamic student data management in colleges and universities. The important semantics of nodes in the network are also captured in this study using fuzzy k-kernel decomposition as a technique for multigranularity partitioning. Based on the SIR model, FHNE compares the production of the sequence to the process of information transmission in the random walk stage, which increases the node sequence’s accuracy. According to the research, the dynamic student data management system that is used in the higher education platform that is suggested in this study can significantly increase the effectiveness of managing student data.
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