To address the limitations of Large Language Models （LLMs） in generating Structured Query Language （SQL） in complex multi-table database scenarios， a multi-generator collaboration-based Text-SQL framework MG-SQL （Multi-Generator SQL） based on collaborative generators was proposed. Firstly， to mitigate noise interference caused by irrelevant schema information， the optimization method for enhancing schema linking process was proposed by generating initial SQLs and combining semantic similarity-based retrieval. Secondly， to improve the quality and diversity of candidate SQLs， a multi-strategy collaborative generation framework was developed on the basis of refined schema： 1） the experience generator was used to retrieve dynamic examples； 2） the chain-of-thought generator was used to strengthen logical reasoning； 3） the query plan generator was used to simulate database execution flows； and 4） the progressive generator was used to perform iterative optimization. Thirdly， the optimal SQL was selected through voting mechanism. Finally， a reflective learning mechanism was further proposed， where the generated results and reference SQL were compared to form reflective samples， so as to construct domain-specific knowledge base dynamically for continuous learning. The BIRD benchmark results demonstrate that， when employing the lightweight GPT-4o-mini model， the proposed framework’s schema linking achieves a 98.89% Strict Recall Rate （SRR） while effectively filtering out 44.91% of irrelevant columns； the SQL generated by the proposed framework achieves a 69.69% EXecution accuracy （EX） and a 79.59% Valid Efficiency Score （VES）， outperforming mainstream GPT-4o-based approaches， which validates the effectiveness of the proposed framework in complex scenarios.