Code comments serve as natural-language descriptions of the source code functionality, helping developers quickly understand the code’s semantics and functionality, thus improving software development and maintenance efficiency. However, writing and maintaining code comments is time-consuming and labor-intensive, often leading to issues such as absence, inconsistency, and obsolescence. Therefore, the automatic generation of comments for source code has attracted significant attention. Existing methods typically use information retrieval techniques or deep learning techniques for automatic code comment generation, but both have their limitations. Some research has integrated these two techniques, but such approaches often fail to effectively leverage the advantages of both methods. To address these issues, this study proposes a semantic reranking-based code comment generation method, SRBCS. SRBCS employs a semantic reranking model to rank and select comments generated by various approaches, thus integrating multiple methods and maximizing their respective strengths in the comment generation process. We compared SRBCS with 11 code comment generation approaches on two subject datasets. Experimental results demonstrate that SRBCS effectively integrates different approaches and outperforms existing methods in code comment generation.