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To solve the problem of insufficient modeling of multi-view spatial interaction in metro stations by traditional methods, this study proposes an Adaptive Multi-view fusion Graph Neural Network Model (AMFGNN) to conduct spatial interaction modeling in metro stations short-term passenger flow prediction. In the spatial dimension, the model includes multiple partial views such as physical topology graph, line accessibility graph, spatial distance graph, etc., and uses the graph attention networks (GAT) to learn the dynamic spatial interaction within a single view. Taking the single-view station as the central node, combined with the station in other views as neighbor nodes, this paper constructs a fused view is and uses the GAT is to learn the dynamic interaction between multiple views. In the time dimension, the gated recurrent unit neural network is used to learn the time-varying characteristics of station passenger flow …