NL2SQL (natural language to structured query language) task aims to translate natural language queries into SQL (structured query language) executable by the database. A Chinese cross-domain NL2SQL algorithm enhanced by auxiliary tasks was proposed. Core idea was to perform multi-task training and improve the accuracy of the model by adding auxiliary tasks in the decoder and combining the prototype model. Auxiliary task was designed by modeling the database schema into a graph, predicting the dependency relations between the natural language queries and the nodes in the database schema graph, and explicitly modeling the dependency relations between the natural language query and the database schema. Through the improvement of auxiliary tasks, the model can better identify which tables/columns in the database schema are more effective for predicting the target SQL for specific natural language queries. Experimental results on the Chinese NL2SQL dataset DuSQL show that the algorithm after adding auxiliary tasks has achieved better results than the prototype model, and can better handle cross-domain NL2SQL task.