城市增长模拟对可持续空间规划与政策制定至关重要。元胞自动机（CA）是其中应用最广泛的方法之一；现有模型可分为基于栅格（RCA）和基于矢量（VCA）两类范式，二者在结构上的差异制约了模型的整合性与可迁移性。本文提出一种通用图结构元胞自动机（UniGCA）框架，以同等支持上述两种范式，并应对空间结构设计与空间交互建模中的挑战。基于UniGCA框架，进一步构建了分区图U-Net元胞自动机（PGUN-CA）模型作为其实用实现。PGUN-CA采用优化的图存储策略与多级图划分方法，以克服大规模城市模拟中的内存与计算约束；同时融合图U-Net与注意力机制，以刻画复杂的空间交互关系。以上海市（栅格数据）与澳大利亚松河流域（矢量数据）为案例的研究表明，PGUN-CA较基线模型精度更高，其指标（Figure of Merit, FoM）分别提升6.96%与9.41%。这些结果凸显了UniGCA框架的通用性与可扩展性，可为异构数据类型下的大规模城市增长模拟提供实用、灵活的解决方案，并为城市规划与可持续发展提供坚实支撑。

Urban growth simulation is crucial for sustainable spatial planning and policy-making. Cellular automata (CA) are among the most widely used approaches, and existing models can be categorized into raster-based (RCA) and vector-based (VCA) paradigms, in which structural differences hinder integration and transferability of available models. Here, a universal graph-based cellular automata (UniGCA) framework is proposed to equally support these two distinct paradigms and address the challenges in spatial structure design and spatial interaction modeling. Building on the UniGCA framework, the Partitioned Graph U-Net Cellular Automata (PGUN-CA) model is developed as a practical implementation. PGUN-CA employs optimized graph storage and multilevel graph partitioning to overcome memory and computational constraints in large-scale urban simulation, while integrating Graph U-Net with attention mechanisms to capture complex spatial interaction. Case studies in Shanghai (raster data) and the Pine Catchment, Australia (vector data), demonstrate that PGUN-CA achieves higher accuracy than baseline models, with figure of merit (FoM) improvements of 6.96% and 9.41%. These findings highlight the generality and scalability of the UniGCA framework, which will provide a practical and flexible solution for large-scale urban growth simulation across heterogeneous data types and solid support for urban planning and sustainable development.