An Artificial Neural Network-Based Knowledge Engineering Model for Predicting Urban–Rural Integration Development

This study develops an artificial neural network-based knowledge engineering model for predicting urban–rural integration development. Urban–rural integration is treated as a complex socio-technical process involving nonlinear interactions among information transfer, knowledge spillover, social capital, and digital infrastructure maturity. Survey data were collected from 759 respondents in four cities of Jiangsu Province, China. A hybrid PLS-SEM–ANN approach was applied, where PLS-SEM validated the theoretical relationships and ANN captured nonlinear predictive patterns. The PLS-SEM results confirm that information transfer enhances knowledge spillover and social capital, which subsequently improve urban–rural integration development. Digital infrastructure maturity strengthens the effect of knowledge spillover but weakens the social-capital pathway. The ANN results demonstrate stable predictive performance across training and testing datasets, with sensitivity analysis identifying social capital and information transfer as key predictors. The study contributes to knowledge engineering by transforming social, informational, and digital indicators into an intelligent predictive framework for decision support.