In　many　fields,　spatiotemporal　prediction　is　gaining　more　and　more　attention,　e.g.,　air　pollution,　weather　forecasting,　and　traffic　forecasting.　Water　quality　prediction　is　a　spatiotemporal　prediction　task.　However,　there　are　several　challenges　in　water　quality　prediction:　1)　Water　quality　time　series　has　a　complex　nonlinear　relationship,　making　it　difficult　to　predict;　2)　Water　quality　sensors　are　distributed　on　the　river　networks　and　have　a　strong　spatial　dependence　on　water　quality　prediction;　and　3)　Poor　long-term　forecast　accuracy.　To　solve　these　problems,　this　work　proposes　a　spatiotemporal　prediction　model　called　a　Fusion　Spatio-temporal　Graph　Convolution　Neural　network　(FSGCN).　First,　This　work　uses　a　temporal　attention　mechanism　to　solve　the　nonlinear　problem　of　water　quality　time　series.　Second,　It　adopts　a　graph　convolution　to　extract　spatial　dependencies　of　river　networks,　and　the　fusion　of　spatiotemporal　can　more　easily　capture　spatiotemporal　features.　Third,　it　adopts　a　temporal　convolution　residual　mechanism,　improving　long-term　series　prediction　accuracy.　This　work　adopts　two　real-world　datasets　to　evaluate　the　proposed　FSGCN,　and　experiments　demonstrate　that　FSGCN　outperforms　several　state-of-the-art　methods　in　terms　of　prediction　accuracy.