University-teaching quality evaluations are crucial for assessing teachers’ effectiveness and enhancing students’ learning in classrooms. To improve the evaluation efficiency, this study suggests a creative classroom evaluation approach by using machine vision and pentapartitioned neutrosophic cubic set (PNCS). First, this study uses machine vision technology to establish a PNCS to capture the students’ states in classrooms. Second, it proposes four entropy functions to determine the attribute weights. Third, it combines the improved entropy weight functions with the PNCS to evaluate the teaching effectiveness. This study’s practical price is to introduce big data theories into teaching evaluation fields. Last, an example is provided to confirm the efficacy and applicability of the evaluation approach suggested in this study.

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