Building shape cognition is essential for tasks, such as map generalization, urban modeling, and building semantics and distribution pattern recognition. Traditional geometric and statistical methods rely on human-defined shape indicators, and spectral-based graph neural networks (GNNs) require Laplacian eigendecomposition, resulting in high algorithmic complexity. Therefore, we proposed a low-complexity and simple-to-use spatial-domain GNN for differentiating building shapes. To examine the influence of the building vertices on their shape, we treated each building as a graph and proposed a graph isomorphic network with weighted multi-aggregators (GIN-WMA) by analyzing the node connectivity of a building graph. The GIN-WMA utilizes a novel aggregator that combines the sum and max aggregators, enhancing its recognition and differentiation capabilities. This approach can effectively differentiate nodes that have identical features after aggregation by the sum aggregator. We extracted features considering both local node and global shape features, drawing inspiration from Gestalt cognitive psychology and GNN's “node–graph” differentiation strategy. In addition, we compared the performance of GIN-WMA with existing methods, studying the effect of various node features and their combinations on classification accuracy. The results demonstrated that GIN-WMA outperforms other methods in discriminating building shapes, demonstrating superior capabilities in shape classification and enabling end-to-end extraction and classification of building shapes.