The challenge of natural language processing is from natural language to logical form (SQL). We present an F-SemtoSql neural approach that is a fuzzy decision semantic deep network query model based on demand aggregation. It aims to address the problem of the complex and cross-domain Text-to-SQL generation task. The corpus is trained as the input word vector of the model with LSTM and Word2Vec embedding technology. Combined with the dependency graph method, the problem of SQL statement generation is converted to slot filling. Complex tasks were divided into four levels via F-SemtoSql and constructed by the need of aggregation. At the same time, to avoid the order problem in the traditional model effectively, we adopted the attention mechanism and used a fuzzy decision mechanism to improve the model decision. On the challenging Text-to-SQL benchmark Spider and the other three datasets, F-SemtoSql achieves faster convergence and occupy the first position.