Construction and Application of Random Forest (RF)-Based Early Childhood Development Assessment Models

Abstract

Abstract Early childhood development (ECD) is an essential foundation for children’s future development and a building block and driving force for society’s future development. Traditional evaluation models of early childhood development focus on children’s comprehensive evaluation, ignoring the importance of each evaluation data. In addition, the previous evaluation model mainly relies on the expert’s experience, which is highly dependent and has a strengthened subjectivity. Therefore, this paper combines the random forest algorithm to construct an early childhood development evaluation model and builds a multilayer evaluation index system. Test experiments show that the evaluation results obtained after the model’s training have a lower error than the traditional evaluation model, and the results are closer to the expected results. Application experiments show that the model can effectively present the evaluation results of children’s abilities and intuitively present comprehensive evaluation results, so that parents and teachers can view the corresponding evaluation data according to the needs of children’s development. At the same time, the evaluation results are consistent with the actual development of children, which can provide parents and teachers with effective and reliable evaluation data to improve children’s development program.