Predicting Priority Factors for Rural E-Commerce Development Using Tri-Stage Integrated Attention Residual Transformer

E-commerce’s quick expansion has changed global marketplaces, creating rural development potential. Rural e-commerce performance relies on technology infrastructure, socio-economic circumstances, and government policy. Despite increased interest in rural e-commerce, current models generally fail to combine these various aspects. This research introduces a new Tri-Stage Integrated Attention Residual Transformer (TIART) model to anticipate rural e-commerce development priorities. Attention mechanisms, residual learning, and transformer topologies improve feature extraction and prediction. The model forecasts key parameters including family income, internet penetration, and logistical performance that are essential for rural e-commerce adoption. The unbalanced, incomplete, and noisy aspects of rural data have challenged traditional models. A three-stage design in the TIART model tackles these issues—residual connections enhance feature stability, attention layers prioritize critical variables, and dimensional mixing improves cross-feature interactions. The model is evaluated on a five-year dataset from rural Bavaria, Germany, consisting of over 150,000 records and 21 features. TIART achieves a prediction accuracy of 98.7% $R^{2}$ , with a root mean square error (RMSE) of 0.98, outperforming XGBoost, EfficientNet, and GhostNet by 4–7% across key metrics. It also reduces execution time by up to 35% compared to deep CNN models. This study shows that powerful machine learning may maximize rural e-commerce growth and equip policymakers and entrepreneurs with actionable insights. The model provides a scalable framework to guide rural intervention and infrastructure development.