Villages areas hold significant importance in the study of human-land relationships. However, with the advancement of urbanization, the gradual disappearance of spatial characteristics and the homogenization of landscapes have emerged as prominent issues. Existing studies primarily adopt a single-disciplinary perspective to analyze villages spatial morphology and its influencing factors, relying heavily on qualitative analysis methods. These efforts are often constrained by the lack of digital infrastructure and insufficient data. To address the current research limitations, this paper proposes a Hierarchical Graph Neural Network (HGNN) model that integrates multi-source data to conduct an in-depth analysis of villages spatial morphology. The framework includes two types of nodes-input nodes and communication nodes-and two types of edges-static input edges and dynamic communication edges. By combining Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), the proposed model efficiently integrates multimodal features under a two-stage feature update mechanism. Additionally, based on existing principles for classifying villages spatial morphology, the paper introduces a relational pooling mechanism and implements a joint training strategy across 17 subtypes. Experimental results demonstrate that this method achieves significant performance improvements over existing approaches in multimodal fusion and classification tasks. Additionally, the proposed joint optimization of all sub-types lifts mean accuracy/F1 from 0.71/0.83 (independent models) to 0.82/0.90, driven by a 6% gain for parcel tasks. Our method provides scientific evidence for exploring villages spatial patterns and generative logic.

翻译：村落区域在人地关系研究中具有重要意义。然而，随着城市化进程的推进，空间特征的逐渐消失与景观的同质化已成为突出问题。现有研究主要采用单一学科视角分析村落空间形态及其影响因素，严重依赖定性分析方法，这些工作常受限于数字基础设施的缺乏与数据不足。为应对当前研究局限，本文提出一种层次图神经网络（HGNN）模型，通过整合多源数据对村落空间形态进行深入分析。该框架包含两类节点——输入节点与通信节点，以及两类边——静态输入边与动态通信边。通过结合图卷积网络（GCN）和图注意力网络（GAT），所提模型在两阶段特征更新机制下高效融合多模态特征。此外，基于现有村落空间形态分类原则，本文引入关系池化机制，并在17个子类型上实施联合训练策略。实验结果表明，该方法在多模态融合与分类任务中较现有方法取得显著性能提升。同时，所提出的全子类型联合优化将平均准确率/F1值从0.71/0.83（独立模型）提升至0.82/0.90，其中地块任务带来6%的性能增益。本方法为探索村落空间格局与生成逻辑提供了科学依据。