The problem of generating structured query language (SQL) from natural language questions (Text-to-SQL) was analyzed. A two-stage framework was proposed to decouple the processes of schema linking and SQL generation in order to reduce the complexity of SQL generation. Database tables, columns, and values mentioned in the question were identified by a schema linker based on relational graph attention network in the first stage. The syntactic structure of the question and the internal relationships between database schema items were used to guide the model in learning the alignment between the question and the database. The original syntactic dependency tree was modified by merging relationships irrelevant to schema linking and adding dependencies between subordinating conjunctions in parallel structures and other elements in the sentence in view of the characteristics of Text-to-SQL task when constructing the question graph, which helps the model capture long-distance dependencies. SQL generation was performed by injecting the alignment information into the T5 encoder and fine-tuning it in the second stage. Experiments were conducted on the Spider, Spider-DK and Spider-Syn datasets. Results showed that the method performed well, especially for Text-to-SQL problems of medium difficulty and above.