建模空间异质性及其相关临界转变，仍是地理学与环境科学中的基础性挑战。尽管传统的地理加权回归（GWR）和深度学习模型提升了预测能力，但它们往往难以阐明状态依赖的非线性关系——即驱动因子在不同异质区域中可能呈现相反的功能作用。我们提出一种受热力学启发的可解释地理空间人工智能框架，将统计力学与图神经网络相融合。该框架将空间变异性概念化为系统负荷（E）与容量（S）之间的热力学竞争，从而解耦驱动空间过程的潜在机制。我们在三个模拟数据集及三个跨领域真实数据集（住房市场、心理健康患病率、野火引发的PM2.5异常）上开展实验，结果表明，新框架成功识别出预测因子在不同状态下的角色反转现象，而标准基线模型均未能发现此类现象。值得注意的是，该框架明确诊断出2023年加拿大野火事件期间系统向负荷主导态的相变过程，从而将物理机制转变与统计异常区分开来。这些发现表明，引入热力学约束可在保持复杂空间系统强预测性能的同时，提升地理人工智能（GeoAI）的可解释性。

Modeling spatial heterogeneity and associated critical transitions remains a fundamental challenge in geography and environmental science. While conventional Geographically Weighted Regression (GWR) and deep learning models have improved predictive skill, they often fail to elucidate state-dependent nonlinearities where the functional roles of drivers represent opposing effects across heterogeneous domains. We introduce a thermodynamics-inspired explainable geospatial AI framework that integrates statistical mechanics with graph neural networks. By conceptualizing spatial variability as a thermodynamic competition between system Burden (E) and Capacity (S), our model disentangles the latent mechanisms driving spatial processes. Using three simulation datasets and three real-word datasets across distinct domains (housing markets, mental health prevalence, and wildfire-induced PM2.5 anomalies), we show that the new framework successfully identifies regime-dependent role reversals of predictors that standard baselines miss. Notably, the framework explicitly diagnoses the phase transition into a Burden-dominated regime during the 2023 Canadian wildfire event, distinguishing physical mechanism shifts from statistical outliers. These findings demonstrate that thermodynamic constraints can improve the interpretability of GeoAI while preserving strong predictive performance in complex spatial systems.