With the development of digital energy governance, reliable measurement of energy economic coupling and low-carbon policy effects requires heterogeneous data evidence. This paper constructs a multi-source data mining framework for provincial energy, industry, economy and carbon indicators. The unified coding module converts 26 indicators in 30 regions from 2011 to 2023 into standardized time feature sequences. The coupled identification module measures the strength, direction and stability of the interaction of energy consumption, economic output, technology input and emission intensity. On this basis, the low-carbon policy effect evaluation model is constructed, and the low-carbon policy variables are mapped into the coupling state and the carbon emission reduction response. In the experiments of 10,140 region-year-indicator feature samples, the proposed model achieves 0.913 recognition accuracy, 0.887 F1-score and 6.42% MAE in coupling recognition and effect measurement. In the coupling strength fitting task, compared with the SVM, Random Forest and LSTM baselines, the MAE is reduced by 14.8%, 11.6% and 7.9%, respectively. The results provide data support for the evaluation of low-carbon policy effect and regional differentiated policy adjustment.
