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Accurate tourism demand forecasting can improve tourism experiences and realize smart tourism. Existing spatial–temporal tourism demand forecasting models only explore pre-specified and static spatial connections across regions without considering multiple or dynamic spatial connections; however, this is not sufficient for modeling actual tourism demand. In this paper, we propose a graph-attention based spatial–temporal learning framework for tourism demand forecasting. A weight-dynamic multi-dimensional graph is organized to embed multiple explicit dynamic spatial connections and provide a node attribute sequence for learning implicit dynamic spatial connections. We further propose a heterogeneous spatial–temporal graph-attention network (called HSTGANet), which is effective in handling both explicit and implicit dynamic spatial connections, learning high-dimensional spatial–temporal features, and …