人类移动行为的精准预测已产生显著的社会经济影响，例如位置推荐与疏散建议。然而，现有方法普遍存在泛化能力不足的问题：单模态方法受限于数据稀疏性与固有偏差，而多模态方法难以有效捕捉由静态多模态表征与时空动态之间的语义鸿沟所导致的移动动态特征。为此，我们引入多模态时空知识以刻画移动动态，用于位置推荐任务，提出\textbf{M}ulti-\textbf{M}odal \textbf{Mob}ility（\textbf{M}$^3$\textbf{ob}）模型。首先，通过利用大语言模型增强的时空知识图谱（STKG）所捕获的功能语义与时空知识，构建统一的时空关系图（STRG）以实现多模态表征。其次，设计一种门控机制融合不同模态的时空图表示，并提出一种基于STKG的跨模态对齐方法，将时空动态知识注入静态图像模态。在六个公开数据集上的大量实验表明，所提方法不仅在常规场景下持续提升性能，且在异常场景下展现出显著的泛化能力。

The precise prediction of human mobility has produced significant socioeconomic impacts, such as location recommendations and evacuation suggestions. However, existing methods suffer from limited generalization capability: unimodal approaches are constrained by data sparsity and inherent biases, while multi-modal methods struggle to effectively capture mobility dynamics caused by the semantic gap between static multi-modal representation and spatial-temporal dynamics. Therefore, we leverage multi-modal spatial-temporal knowledge to characterize mobility dynamics for the location recommendation task, dubbed as \textbf{M}ulti-\textbf{M}odal \textbf{Mob}ility (\textbf{M}$^3$\textbf{ob}). First, we construct a unified spatial-temporal relational graph (STRG) for multi-modal representation, by leveraging the functional semantics and spatial-temporal knowledge captured by the large language models (LLMs)-enhanced spatial-temporal knowledge graph (STKG). Second, we design a gating mechanism to fuse spatial-temporal graph representations of different modalities, and propose an STKG-guided cross-modal alignment to inject spatial-temporal dynamic knowledge into the static image modality. Extensive experiments on six public datasets show that our proposed method not only achieves consistent improvements in normal scenarios but also exhibits significant generalization ability in abnormal scenarios.