Supply chain demand forecasting is fundamental to supply chain management. Precise forecasts optimize inventory, cut costs, and boost efficiency. However, traditional methods struggle in existing scenarios. They can’t clearly define hierarchical relationships among supply chain enterprises, leading to information transfer and integration issues. Also, they fail to adapt quickly to dynamic changes like adjusted cooperation and logistics route shifts. Therefore, this paper proposes a supply chain demand forecasting model based on hierarchical attention mechanism and dynamic graph neural network. The hierarchical attention mechanism is used to deeply explore the relationships within and across the levels of the supply chain, helping the model understand complex structures. The dynamic graph structure learning module tracks the changes in the supply chain in real time and adaptively adjusts the graph structure. Additionally, we expand the model into a probabilistic model to quantify the uncertainty of predictions, providing more information for decision-making. We conducted experiments on the SupplyGraph dataset, comparing the performance of our model with several GNN baseline models. The experimental results demonstrate that our model outperforms the baseline model in RMSE, MAE and other metrics.