The Knowledge Graph (KG) augmented recommendation mitigates the cold start issue by exploiting complex semantic clues in users’ behaviors. However, existing methods focus on bringing these auxiliary knowledge into the user-item interaction space, ignoring the gap between different sources. In this study, we propose a knowledge-aware intent subgraph learning (KISL) which mines users’ intents with KG to promote fine-grained interest learning for personalized recommendation. We model each intent representation as an attentive combination of KG relations on the knowledge graph. Guided by the intents, KISL devises a dimensional disentanglement to divide the interaction graph into several augmented intent-aware subgraphs. Fine-grained personalized embedding is learned during subgraph message propagation to predict users’ interactions. Extensive experiments on two public datasets demonstrate the effectiveness of KISL by knowledge-aware intent modeling over baselines.