Path Identification of Digital Inclusive Finance Driving Industrial Upgrading Based on Deep Learning

With the rapid development of intelligent communication networks and Electromagnetic Waves, Antennas and Propagation technologies, spatiotemporal information modeling and dynamic topology analysis have become fundamental requirements for large-scale heterogeneous data processing and distributed decision-making. To address the challenge of identifying complex transmission mechanisms in digital inclusive finance, this study proposes an Attention-based Spatio-Temporal Graph Convolutional Network (ASTGCN) that integrates graph convolution, temporal convolution, and multi-head attention for dynamic path recognition. By constructing multidimensional spatiotemporal graphs from multi-source panel data and jointly modeling geographic and economic relationships, the proposed framework captures nonlinear spatial spillover effects and long-term temporal dependencies while enabling interpretable quantification of key transmission paths. Empirical results demonstrate that technological innovation represents the dominant driving channel with a relative contribution of 38.6%, while the proposed ASTGCN model reduces root mean square error and mean absolute percentage error by 18.4% and 21.2%, respectively, compared with the standard LSTM baseline. Furthermore, the model successfully reconstructs regional information propagation topology and reveals dynamic transitions in transmission mechanisms through attention-based path weighting. The proposed architecture provides an effective engineering framework for spatiotemporal graph learning, distributed information propagation, and adaptive network analysis, offering valuable methodological references for intelligent sensing, communication-oriented monitoring, and Electromagnetic Waves, Antennas and Propagation applications.