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.　Note　to　Practitioners—This　work　considers　the　critical　problem　of　spatiotemporal　water　quality　prediction.　Accurate　water　quality　prediction　can　effectively　prevent　environmental　pollution.　Traditional　water　quality　prediction　only　focuses　on　time　series　features　without　considering　spatial　features.　In　this　work,　a　novel　spatiotemporal　prediction　approach　is　proposed　that　combines　spatial-temporal　graph　fusion　construction　networks　for　water　quality　time　series　prediction　in　a　real-time　manner.　This　work　shows　that　this　approach　can　achieve　longer　forecasting　sequences　and　more　accurate　results　than　traditional　forecasting　methods.　As　a　practical　consequence　of　this　research,　spatiotemporal　graph　fusion　convolution　networks　for　water　quality　prediction　can　effectively　integrate　multi-dimensional　data　and　improve　the　accuracy　of　long-term　water　quality　prediction.　This　approach　can　also　be　applied　to　other　fields,　including　intelligent　transportation,　smart　manufacturing,　finance,　the　Internet　of　Things,　and　urban　computing.　IEEE