Author(s)

Jun Feng and Mu Yang

Corresponding Author

Mu Yang

Abstract

In this paper, an algebraic feature intelligent decision-making classification algorithm based on Schmidt orthogonal optimization is constructed. Firstly, Schmidt method is used to orthogonally optimize the classification vectors, then singular value decomposition is applied to the optimized vectors, and finally, Euclidean distance is used to complete the classification of objects. In order to verify the correctness of the algorithm, we apply this intelligent decision classification algorithm to formative evaluation. The experimental results show that the algorithm eliminates the influence of interference information in the evaluation process on the classification accuracy of final evaluation, and greatly improves the accuracy of formative evaluation of teaching.

Keywords

Schmidt orthogonal optimization; Singular Value decomposition; Intelligent decision making; Formative evaluation