Leveraging Digital Tools for Optimized Preschool Education Teaching Quality Analysis Across Urban and Rural Areas

Under the background of deepening “double reduction” and the digitalization of education, the imbalance between urban and rural preschool teachers has become a key bottleneck in improving the quality of education in Fushun. Based on teacher data from the statistical yearbook; Global Human Settlement Layer (GHSL); and United Nations Educational, Scientific, and Cultural Organization (UNESCO); this paper constructed a five-dimensional digital literacy index and quantified teacher literacy based on the Exploratory Structural Equation Modeling (ESEM) and entropy weight-analytic hierarchy process model. It identified hot spots of teacher demand through spatial analysis and established an improved non-dominated sorting genetic algorithm-II multi-objective optimization model based on equity, cost, and literacy gains. After Unity-geographic information system digital twin simulation and field pilot cross-validation, the results indicate that this approach significantly improves allocation efficiency, reduces the urban–rural gap, promotes teacher development, and ensures data security (de-identification and hierarchical access) and forward-looking (embedded career life cycle), providing a replicable and traceable decision-making paradigm for local education governance.