Zhu TianyouThis email address is being protected from spambots. You need JavaScript enabled to view it., Qi Yaru, Jiang Kongchen, Sang Yanting, and Yang Chao
State Grid Information & Telecommunication center (Big Data center), Beijing 100052, P. R. China
Received: October 13, 2025 Accepted: November 16, 2025 Publication Date: December 28, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
In real-world database applications, SQL statements written by users often create performance bottlenecks because they violate best-practice rules. Traditional rule-based detectors have limited ability to recognize diverse and increasingly irregular statements and are costly to maintain. To address this, we propose SLQ, a two-stage intelligent SQL optimization framework. First, a lightweight stacked-LSTM module pinpoints problematic statements; then a pre-trained large language model, Qwen3, automatically generates explanations for each f law and offers targeted rewrite suggestions, helping users quickly improve query quality. Evaluated on a standard dataset, SLQ achieves accuracy, precision, recall and F1 of 0.9841, 0.9974, 0.9702 and 0.9836 respectively, demonstrating superior detection and optimization capability and markedly enhancing SQL compliance and execution efficiency.
Keywords: SQL Query Optimization; Large Language Model (LLM); Long Short-Term Memory(LSTM)
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