To address the issues of high labor and time costs, neglect of geographic spatial interactions, and coarse handling of high-dimensional unstructured features in existing retail chain store site selection methods, this paper proposes a smart site selection model (SmartSite) that integrates multi‑source spatiotemporal knowledge graphs and graph neural networks (GNNs). Specifically, a multi‑source spatiotemporal database (MSSTDB) is first established to collect and standardize multi‑source spatiotemporal data. Subsequently, a multi‑source spatiotemporal knowledge graph (MSSTKG) is constructed to accurately extract spatiotemporal entities and their relationships, and a graph convolutional network (GCN) is employed to derive deep feature representations from geographic data. Finally, an attention mechanism is introduced to dynamically assign feature weights, thereby building an intelligent site selection model. The performance of the proposed model is validated through comparative experiments on benchmark datasets and ablation studies, and the model is further applied to a leading new retail chain enterprise's store location scenario. Experimental results show that, compared with traditional manual approaches and conventional data‑driven methods, the proposed model can effectively exploit the correlations inherent in geographic spatial data, significantly reduce site selection costs, and deliver substantial economic benefits, offering a reliable decision‑making reference for retail chain enterprises in store location planning.

针对现有的零售连锁门店选址方法存在人力与时间成本高、忽略地理空间交互关系以及高维非结构化特征处理粗糙的问题, 提出一种融合多源时空知识图谱与图神经网络(GNN)的连锁门店智慧选址模型(SmartSite)。首先, 搭建多源时空数据库(MSSTDB), 完成多源时空数据的采集与规范化治理; 然后, 构建多源时空知识图谱(MSSTKG), 实现时空实体与时空关系的精准抽取, 并依托图卷积网络(GCN)挖掘地理数据的深层特征表示; 最后, 引入注意力机制动态分配特征权重, 构建智能化选址模型。通过标准数据集对比实验与消融实验验证模型性能, 并将该模型应用于头部新零售连锁企业的门店选址场景。结果表明, 相较于传统的人工选址及常规数据驱动选址方法, 该模型可充分挖掘地理空间数据的关联特性, 显著地降低选址成本, 产生良好的经济效益, 可为零售连锁企业门店选址决策提供决策参考。