Abstract
Using the Gradient boost decision tree (GBDT) algorithm, the classification problem of children's cognitive level of mathematical knowledge is transformed into the classification problem in machine learning. Kindergarten children's cognitive difficulty with different mathematical knowledge modules is different. Each knowledge module can be abstracted into several basic skill points, and all knowledge modules and basic skill points form a knowledge skill matrix. In this study, based on the teaching textbooks of a large class in a kindergarten, all mathematical knowledge modules are decomposed into several basic skill points, and the knowledge skill matrix is constructed. Then, based on the children's learning data collected in the actual teaching activities, two classification models of children's mathematical knowledge and skills are constructed by using the GBDT algorithm. The two models can be applied to practical teaching. Mining children's cognitive law of mathematical knowledge help teachers design reasonable psychological intervention mechanisms and improve children's cognition level.
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