Knowledge-intensive Questions typically require Large Language Models (LLMs) to retrieve external knowledge beyond their parametric memory to generate factually accurate and human-aligned answers. Retrieval-Augmented Generation (RAG), a reliable technique for supplementing LLMs with external information, enhances generation quality and mitigates hallucination by incorporating retrieved knowledge into the reasoning process. However, existing multi-step retrieval RAG methods are prone to introducing a large number of irrelevant documents during deep exploration of external knowledge bases and remain constrained by one-sided exploration strategies. This hinders effective exploration and utilization of high-quality knowledge, ultimately leading to unreliable reasoning and answers. To this end, we propose a novel Hybrid Knowledge-Aware RAG (HyKAG) framework for knowledge-intensive questions. Specifically, to enable deeper exploration of high-quality external knowledge and enhance the model’s knowledge awareness, we first propose hybrid knowledge expansion and refinement modules that enrich retrieved content from dual retrieval perspectives and refine it through an incremental cross-step integration strategy. Furthermore, we introduce a hybrid knowledge-aware adaptive retrieval module that formulates high-quality retrieval decisions by leveraging the refined hybrid knowledge, thereby facilitating deeper knowledge exploration. Extensive empirical results on four datasets demonstrate the superiority of HyKAG.