In　the　crowd　navigation,　reinforcement　learning　based　on　graph　neural　network　is　a　promising　method,　which　effectively　solves　the　poor　navigation　effect　based　on　social　interaction　model　and　the　freezing　behavior　of　robot　in　extreme　cases.　However,　since　the　information　correlation　of　human　trajectory　has　not　been　involved　in　the　method,　its　performance　still　needs　improvement.　Therefore,　we　proposed　a　deep　reinforcement　learning　model　based　on　Social　Spatial-Temporal　Graph　Convolution　Network　(SSTGCN)　to　handle　the　crowd　navigation　problem,　in　which　the　spatial-temporal　information　of　human　trajectory　has　been　taken　advantage　to　predict　human　behavior　intentions　and　help　robot　plan　path　more　efficiently.　The　model　consists　of　graph　learning　module　and　robot　forward　planning　module.　In　the　graph　learning　module,　the　latent　features　of　agents　are　taken　advantage　to　reason　about　the　relations　among　the　agents,　and　SSTGCN　is　used　to　update　feature　matrix.　In　addition,　value　estimation　module　calculates　state　representation　and　state　prediction　module　predicts　the　next　state.　The　robot　forward　planning　module　makes　use　of　k-step　planning　to　estimate　the　quality　of　state　and　searches　the　best　k　steps　planning.　We　tested　our　model　in　the　Crowd-Nav　platform,　and　the　results　show　that　our　model　has　high　navigation　success　rate　and　short　navigation　time.　In　addition,　it　has　good　robustness　to　crowd　changes.