Spatial　architecture　neural　network　(SANN),　which　is　inspired　by　the　connecting　mode　of　excitatory　pyramidal　neurons　and　inhibitory　interneurons　of　neocortex,　is　a　multilayer　artificial　neural　network　and　has　good　learning　accuracy　and　generalization　ability　when　used　in　real　applications.　However,　the　backpropagation-based　learning　algorithm　(named　BP-SANN)　may　be　time　consumption　and　slow　convergence.　In　this　paper,　a　new　fast　and　accurate　two-phase　sequential　learning　scheme　for　SANN　is　hereby　introduced　to　guarantee　the　network　performance.　With　this　new　learning　approach　(named　SFSL-SANN),　only　the　weights　connecting　to　output　neurons　will　be　trained　during　the　learning　process.　In　the　first　phase,　a　least-squares　method　is　applied　to　estimate　the　span-output-weight　on　the　basis　of　the　fixed　randomly　generated　initialized　weight　values.　The　improved　iterative　learning　algorithm　is　then　used　to　learn　the　feedforward-output-weight　in　the　second　phase.　Detailed　effectiveness　comparison　of　SFSL-SANN　is　done　with　BP-SANN　and　other　popular　neural　network　approaches　on　benchmark　problems　drawn　from　the　classification,　regression　and　time-series　prediction　applications.　The　results　demonstrate　that　the　SFSL-SANN　is　faster　convergence　and　time-saving　than　BP-SANN,　and　produces　better　learning　accuracy　and　generalization　performance　than　other　approaches.　©　2014　Elsevier　B.V.　All　rights　reserved.