Supporting Information.

The natural gas supply crisis triggered by the Russia–Ukraine conflict has laid bare the energy market’s extreme vulnerability in the face of geopolitical risk, highlighting the need for accurate multi-step gas price forecasting. However, most AI-based energy price studies have a gap: they focus on single-step prediction or homogeneous model comparisons, lacking analysis of performance degradation in multi-step dynamic frameworks. This study takes daily natural gas price data from the Henry Hub in the United States from 1997 to 2024 as the research object, constructs a multi-step prediction framework with a step size ranging from 1 to 4 days, and systematically compares the prediction performances of four artificial intelligence models: feedforward neural network, support vector machine, random forest, and long short-term memory network. The quantitative results show that, across all prediction cycles, the long short-term memory model has the lowest error rate. For example, in one-step forecasting, its Mean Absolute Percentage Error is 8.53%. Practically, the findings matter. Taking European governments facing natural gas shortages in the Russia-Ukraine conflict as an example, LSTM models can be used for multi-step prediction to forecast price fluctuations 2–4 days in advance, optimizing import reserve strategies to avoid supply disruptions; energy traders can use this to design robust futures arbitrage portfolios. In summary, the research provides a scientific basis and reference for government energy security policy-making and institutional investor trading.