In recent years, with the large increase in data-centric applications, graph data models have gradually attracted people's attention, and the development of graph databases is also very rapid. Users are often more concerned about their efficiency in using databases. This work mainly studies how to use the existing information to query and predict the graph database, so as to preload and cache the data, and improve the response efficiency of the system. In order to make the method cross-data portable and dig deep into the connections between the data, this study extracted SparQL queries into the form of sequences, used the Seq2Seq model to analyze and predict its data, and tested the method using real data sets. Experiments show that the proposed scheme in this study has a sound effect.