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