The ubiquitous deployment of cameras has led to explosive growth of video data, creating an urgent need to explore valuable content. Single-level queries are insufficient to extract comprehensive information, raising the demand for multi-level queries (existence, dynamic, similarity) within a single unified system. However, limited by the high complexity and redundancy of video, existing systems usually support single-level queries, while Vision-Language Models that support multi-level queries incur prohibitive computational overhead, making them infeasible for large-scale video datasets. To address these issues, we propose Craw, a framework for efficient multi-level queries on large-scale video datasets. Specifically, Craw (1) designs the Video Semantic Unit to encapsulate video semantics, (2) develops a semantic-preserving video segmentation algorithm, and (3) constructs a hybrid index framework integrating an inverted index with a cluster index layer for efficient query execution. Experimental results show that Craw outperforms the state-of-the-art (SOTA) by reducing query latency up to two orders of magnitude, while effectively supporting multi-level queries.