SQL query comprehension is a significant challenge in database and data analysis environments due to complex syntax, diverse join types, and deep nesting. Despite its critical role in backend development and data science, many queries, particularly within legacy systems, often lack adequate comments, which severely hinders code readability, maintainability, and knowledge transfer. Existing approaches to automated SQL comment generation face two main challenges: limited training datasets that inadequately represent real-world analytical queries involving multi-table joins, window functions, and complex aggregations, and an insufficient understanding of SQL-specific logical semantics and schema-related context by Large Language Models (LLMs), even after standard training. Our empirical analysis shows that even after continual pre-training and supervised fine-tuning, LLMs struggle to precisely understand complex SQL semantics, leading to inaccurate or incomplete comments. To address these challenges, we propose SQL-Commenter, an advanced comment generation method based on LLaMA-3.1-8B. First, we construct a comprehensive dataset containing longer, more complex SQL queries with expert-verified, detailed comments. Second, we perform continual pre-training using a large-scale SQL corpus to enhance the LLM’s understanding of SQL syntax and semantics. Then, we conduct supervised fine-tuning with our high-quality dataset. Finally, we introduce Direct Preference Optimization (DPO), which leverages human feedback to significantly improve comment quality. SQL-Commenter utilizes a preference-based loss function that encourages the LLM to increase the probability of preferred outputs while decreasing the probability of non-preferred outputs, thereby enhancing both fine-grained semantic learning, such as distinguishing between different join types, and context-dependent quality assessment based on business logic. We evaluate SQL-Commenter on the authoritative Spider and Bird benchmarks, where it significantly outperforms state-of-the-art baselines. On average, across these datasets, our method surpasses the strongest baseline (Qwen3-14B) by 9.29, 4.99, and 13.23 percentage points on BLEU-4, METEOR, and ROUGE-L, respectively. Moreover, human evaluation demonstrates the superior quality of comments generated by SQL-Commenter in terms of correctness, completeness, and naturalness.