To address the challenges of insufficient label semantic understanding， vague relationship modeling， and high computational costs of Large Language Models （LLMs） in zero-shot re-ranking tasks， a hierarchical filtering and label semantic extension method named HFLS （Hierarchical Filtering and Label Semantics） was proposed. In the method， by constructing a multi-level label semantic extension path， a progressive prompting strategy “keyword matching → semantic association → domain knowledge integration” was designed to guide LLMs in deep relational reasoning. At the same time， a hierarchical filtering mechanism was introduced to reduce computational complexity while retaining high-potential candidate documents. Experimental results indicate that on seven benchmark datasets such as TREC-DL2019， HFLS achieves average gains of 21.92%， 13.43% and 8.59%， respectively， in NDCG（Normalized Discounted Cumulative Gain）@10 compared to Pointwise methods like Pointwise.qg， Pointwise.yes_no， and Pointwise.3Label. In terms of reasoning efficiency， HFLS has the processing latency per query reduced by 91.06%， 68.87% and 33.54% compared to Listwise， Pairwise， and Setwise methods， respectively.