The　graph　convolution　network　(GCN),　whose　flexible　convolution　kernels　perfectly　adapt　to　the　complex　topology　of　the　road　network,　has　gradually　dominated　the　spatiotemporal　dependency　learning　of　traffic　flow　data.　Defining　and　learning　the　spatiotemporal　characteristics　and　relationships　of　the　traffic　network　efficiently　and　accurately,　which　are　the　important　prerequisites　for　the　success　of　the　GCN,　have　become　one　of　the　most　burning　research　problems　in　the　field　of　intelligent　transportation　systems.　This　paper　proposes　a　fast　spatiotemporal　learning　(FSTL)　framework　containing　the　fast　spatiotemporal　GCN　module,　which　reduces　the　computational　complexity　of　the　spatiotemporal　GCN　from　O(k(2))　to　O(k)　,　where　k　is　the　number　of　time　steps　of　data　learned　in　each　GCN　operation.　To　mine　globally　and　fast　the　correlations　of　road　node　pairs,　a　correlation　analysis　based　on　the　normal　distribution　with　the　complexity　of　O(N)　,　where　N　is　the　number　of　nodes　in　the　traffic　network,　is　proposed　to　construct　the　global　correlation　matrix.　Besides,　the　multi-scale　temporal　learning　is　integrated　into　the　FSTL　to　overcome　the　receptive　field　constraints　of　the　spatiotemporal　GCN.　The　experimental　results　on　four　real-world　datasets　demonstrate　that　the　FSTL　achieves　48.88%　and　5.26%　reductions　in　the　training　time　and　mean　absolute　error,　respectively,　compared　with　the　state-of-the-art　model.