Application of Artificial Intelligence Discriminant Models in Educational Reform

Classroom teaching evaluation is a key link in improving the quality of education and teaching and optimizing teaching management. Based on the artificial intelligence discriminant model, this paper explores the specific application of mathematical models in classroom teaching evaluation, aiming to reduce the influence of subjective factors through a data-driven approach, and at the same time, construct a scientific evaluation index system and weight allocation mechanism. This study adopts a multivariate statistical method combining cluster analysis and discriminant analysis to systematize the teaching evaluation data, so as to realize a more scientific and objective teaching evaluation system. This study provides new theoretical and practical support for educational management departments and schools, and promotes the reform and innovation of classroom teaching evaluation.