【摘要】 要：针对汽车售后配件供应网络运行复杂，配件需求预测精度不足、响应不及时，售后服务效率较低等问题，提出一种基于时空图卷积的汽车配件供应链需求预测方法。将汽车配件销售网络的销售数据构建为异构图，融合配件销量、金额等节点特征构建多维节点依赖关系；设计图卷积神经网络的节点迭代更新机制，结合长短时记忆神经网络捕获时序特征，利用时空注意力将时间特征与空间特征融入更新后的节点。仿真实验表明：该方法最优RMSE达到2.09，在动态扰动下误差峰值控制在3.44以内，恢复步数不超过50步，验证了该方法在复杂供应链环境下的预测效果和动态适应性。更多还原

【Abstract】 To address complex automotive after-sales parts supply network operations with insufficient demand forecasting accuracy, slow response, and low service efficiency, this study proposed a spatiotemporal graph convolution-based method for automotive parts supply chain demand forecasting. Sales network data of the automotive parts sales network was constructed as a heterogeneous graph, integrating node features like parts sales volume and value to build multi-dimensional node dependencies. A node update mechanism of the graph convolutional neural network was designed, combined with long short-term memory neural networks to capture temporal features, using spatiotemporal attention to integrate temporal and spatial features into updated nodes. Simulation experiments show the method achieves an optimal RMSE of 2.09, error peaks within 3.44 under dynamic disturbances, and recovery within 50 steps, validating its forecasting performance and dynamic adaptability in complex supply chain environments.更多还原