Global　positioning　system　(GPS)　trajectory　map　matching　projects　GPS　coordinates　to　the　road　network.　Most　existing　algorithms　focus　on　the　geometric　and　topological　relationships　of　the　road　network,　while　did　not　make　full　use　of　the　historical　road　network　information　and　floating　car　data.　In　this　study,　the　authors　proposed　a　deep　learning　enabled　vehicle　trajectory　map-matching　method　with　advanced　spatial-temporal　analysis　(DST-MM).　The　algorithm　mainly　focused　on　the　following　three　aspects:　(i)　analyse　the　spatial　relevancy　from　the　prospective　of　geometric　analysis,　topology　analysis　and　intersection　analysis;　(ii)　to　make　full　use　of　the　historical　and　real-time　data,　a　deep　learning　model　was　conducted　to　extract　the　road　network　and　vehicle　trajectory　features　and　(iii)　establish　a　speed　prediction　model　and　nest　it　in　the　temporal　analysis　structure.　It　narrows　down　the　path　search　range　through　establishing　the　dynamic　candidate　graph.　Experimental　results　show　that　the　proposed　DST-MM　algorithm　outperforms　the　existing　algorithms　in　terms　of　matching　accuracy　for　low-sampling　frequencies　GPS　data,　especially　in　the　central　urban　area.