  The fundamental goal of the Text-to-SQL task is to translate natural language
question into SQL query. Current research primarily emphasizes the information
coupling between natural language questions and schemas, and significant
progress has been made in this area. The natural language questions as the
primary task requirements source determines the hardness of correspond SQL
queries, the correlation between the two always be ignored. However, when the
correlation between questions and queries was decoupled, it may simplify the
task. In this paper, we introduce an innovative framework for Text-to-SQL based
on decoupling SQL query hardness parsing. This framework decouples the
Text-to-SQL task based on query hardness by analyzing questions and schemas,
simplifying the multi-hardness task into a single-hardness challenge. This
greatly reduces the parsing pressure on the language model. We evaluate our
proposed framework and achieve a new state-of-the-art performance of
fine-turning methods on Spider dev.
