人工智能技术的广泛应用正在重塑地理学科，其深层挑战在于AI背后体现的计算思维如何与地理空间思维实现深度融合。本文参考“空间计算思维”模型和“编码地理”倡议的相关成果，构建了面向高校地理专业的计算思维与地理空间思维“双维融合”理论框架，揭示了两种思维在认识论、方法论、能力层和创新层四个维度的差异与互补关系。同时，本文提出“问题地理化—方法计算化—意义再地理化”三阶段教学路径，旨在引导学生在结果输出与空间解释的往复对话中实现两种思维的协同运作。在此基础上，本文以经济地理学“产业空间集聚”课程模块为例进行案例阐释。研究表明，这一路径有助于学生从“会操作工具”走向“会用思维思考地理问题”，为应对技术迭代背景下高校地理人才培养的挑战提供了可行性方案。

The widespread application of artificial intelligence is reshaping the discipline of geography, yet the deeper challenge lies in how the computational thinking embodied in AI can be deeply integrated with geospatial thinking. Drawing on the outcomes of the“spatial computational thinking”model and the“Encoding Geography”initiative, this paper constructs a theoretical framework of“dual-thinking integration”of computational thinking and geospatial thinking for geography education in universities. The framework reveals the diff erences and complementarities between the two types of thinking across four dimensions: epistemology, methodology, capability, and innovation. Furthermore, it proposes a three-stage teaching pathway—“geographic problem formulation, computational method implementation, and geospatial meaning reconstruction”—aimed at guiding students towards the synergistic operation of the two kinds of thinking through iterative dialogue between output results and spatial interpretation. A case study based on an economic geography module titled “Industrial Spatial Agglomeration”illustrates that this pathway helps students move from merely“operating tools” to“thinking geographically with tools,”thereby offering a feasible approach to addressing the challenges of cultivating geography talents in the context of rapid technological iteration.