This study investigates how to align knowledge graph with user preferences to enhance collaborative filtering recommendations. Existing knowledge-enhanced methods often rely on single-scenario neighbor aggregation, limiting their ability to capture diverse and uncertain user intents in real-world settings. To address these limitations, we propose MS-KIM, a Multi-scenario Knowledge-enhanced Latent Intent Modeling framework. At its core, MS-KIM formulates user intents as latent variables inferred through variational inference based on interactions and scenario-aware knowledge signals. Specifically, we first design a scenario-aware knowledge aggregation module to extract informative item representations under different scenarios. Building upon these signals, we leverage variational inference to jointly model user intents across multiple scenarios. Moreover, since scenario labels are often incomplete, we introduce a scenario-aware self-supervised task that promotes representation separation across scenarios by leveraging augmented subgraph-based user representations as guidance. Extensive experiments on three real-world datasets demonstrate that our model outperforms strong baselines.
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