Research on the Application of Tree Model in Transforming Complex Natural Language Query Into Sql

With the increasing complexity of database query requirements, the technology of converting natural language query into SQL (NL2SQL) becomes particularly important. The tree model simulates the generation process of SQL statements by constructing a tree structure, and effectively handles complex queries such as multi-table joins, subqueries and aggregate functions. The system proposed in this study firstly pre-screens the database, and uses TF-IDF algorithm and cosine similarity calculation to determine the most relevant tables and fields. Then, the complex SQL statements are disassembled into multi-tree forms, and the tree structure is optimized through syntax analysis and tree pruning reconstruction. A tree model based on deep learning, namely Tree-LSTM, is adopted to predict the components of SQL. The experiment uses Chinese DuSQL data set, and the results show that the model achieves 85.6% accuracy and 84.0% F1 score on the whole data set, especially on complex queries. The advantage of tree model lies in its clear structure, powerful complex query processing ability and cross-domain adaptability. This study provides a new solution for the field of database query, lowers the threshold of database query and improves the efficiency of user query.