As global energy markets grow more complex and volatile, existing forecasting accuracy and policy effect evaluations remain insufficient.An urgent need exists for a precise prediction model integrating legal policies and multi-dimensional energy market data.This paper proposes an LSTM-based model to predict legal regulation effects on energy prices: it uses semantic feature extraction for policy texts, builds a spatiotemporal fusion framework for multi-source heterogeneous data, and designs a hierarchical memory unit's dynamic regulation module to adapt to stage-specific policy adjustments.In historical data, the model's training-set MAE declined fluctuatingly: 56.72 CNY/ton standard coal (vague initial policies) dropped to 28.9 CNY (refined policies).After tiered pricing, policy-related price volatility fell to 45.6%.Policy clarity correlates positively with model accuracy; refinement reduced LSTM's price trend capture error by 66.6%.
Zhao et al. (Thu,) studied this question.