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. This study aims to forecast 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 improve input features, then attention integration prioritizes important features. Dimensional mixing improves feature interactions at the final stage. The suggested strategy is tested using a dataset from rural Bavaria, Germany, where rural e-commerce is pushed. TIART surpasses XGBoost, EfficientNet, and GhostNet in prediction accuracy, computational efficiency, and scalability. This study shows that powerful machine learning may maximize rural e-commerce growth and give policymakers and entrepreneurs with practical information. The model helps guide rural intervention.