Commit messages are natural language text that describe code changes and are crucial for understanding code and its evolution. Constrained by software development costs and related factors, developers often fail to carefully craft commit messages, resulting in unsatisfactory quality in existing software projects. As a result, commit message generation has gained widespread attention. Current work mainly involves collecting commit message data from open-source software projects and training deep learning models on this data to accomplish the task. However, due to the quality issues of open-source project data, existing methods struggle to achieve satisfactory performance. Large language models, by pre-training on vast amounts of code and text data, learn rich linguistic patterns and world knowledge, enabling the generation of high-quality, natural, and contextually appropriate text, thus providing new directions for commit message generation. This study proposes a commit message generation method based on large language models, in which large language models are applied to commit message generation through techniques such as in-context learning and model fine-tuning. Two example retrieval methods are employed to enhance the in-context learning approach. The generated text is then analyzed in terms of naturalness and relevance, and the strengths and limitations of the large language model-based methods, as well as ways to address these limitations, are examined. Experiments demonstrate that large language models outperform baseline methods. In addition, chain-of-thought reasoning and larger-scale models can address the issue of insufficient understanding of code changes.