Abstract
Handmade is an important part of preschool education, which was aimed at improving children's ability to work with their hands. Preschool education is the most basic and important aspect of a country's educational system. As a result, individuals pursuing a degree in preschool education take on a lot of responsibility. The preschool education handmade course has become an important component of preschool education due to its practicality and creativity. Preschool education major offers classes in traditional crafts such as paper cutting, paper dyeing, origami, paper three-dimensional modeling, and ornamental painting. The teaching methods for custom-made preschool education courses are always evolving with the progress of society. The question of how to assess the efficacy of unique teaching methodologies for handmade courses has become crucial. This study employs artificial intelligence to create a neural network for assessing the creativity of teaching approaches for handmade courses in preschool education. The following is the specific work: Firstly, the idea, as well as the benefits and drawbacks of genetic algorithms, is investigated. To build an improved genetic algorithm (IGA), the chromosome encoding, fitness function, and three operation options are enhanced. Secondly, by improving the genetic algorithm, the selection of weights and thresholds in the BP neural network model is improved, and a combined model (IGA-BP) is created by integrating the improved genetic algorithm with the BP network. Finally, rigorous and systematic tests confirm the work's efficacy and viability.