Traditional product demand forecasting has typically been modeled as a single time series problem relying exclusively on temporal information. However, temporal features alone are insufficient to capture complex demand dynamics in modern, interconnected markets. To address this limitation, we propose a product demand forecasting method based on a Spatiotemporal Hypergraph Attention Network (STHA), which jointly models temporal dependencies and higher-order spatial interactions among multiple market entities to enhance forecasting accuracy and robustness. STHA integrates a Long Short-Term Memory (LSTM) network with a Hawkes attention mechanism to capture temporal patterns and constructs a hypergraph to represent multi-entity relationships. It further incorporates hypergraph convolution and a hypergraph attention mechanism to dynamically aggregate higher-order spatial information and weight relational importance. Experiments on the Corporación Favorita sales dataset demonstrate that STHA substantially outperforms single time series benchmarks (ARIMA, LSTM, TCN, Transformer, and PatchTST), achieving notable reductions in MAE, RMSE, and MAPE—with improvements in MAPE exceeding 15 percentage points for certain stores. Compared with the graph-based STGCN model, STHA also exhibits superior robustness. These results demonstrate the effectiveness of STHA for complex multi-market-entity demand forecasting and highlight its potential as a reliable framework for improving inventory management and supply chain decision-making.