Ant　colony　optimization　(ACO)　algorithm　is　widely　used　in　the　instant　delivery　order　scheduling　because　of　its　distributed　computing　capability.　However,　the　order　delivery　efficiency　decreases　when　different　logistics　statuses　are　faced.　In　order　to　improve　the　performance　of　ACO,　an　adaptive　ACO　algorithm　based　on　real-time　logistics　features　(AACO-RTLFs)　is　proposed.　First,　features　are　extracted　from　the　event　dimension,　spatial　dimension,　and　time　dimension　of　the　instant　delivery　to　describe　the　real-time　logistics　status.　Five　key　factors　are　further　selected　from　the　above　three　features　to　assist　in　problem　modeling　and　ACO　designing.　Second,　an　adaptive　instant　delivery　model　is　built　considering　the　customer＇s　acceptable　delivery　time.　The　acceptable　time　is　calculated　by　emergency　order　mark　and　weather　conditions　in　the　event　dimension　feature.　Third,　an　adaptive　ACO　algorithm　is　proposed　to　obtain　the　instant　delivery　order　schedules.　The　parameters　of　the　probability　equation　in　ACO　are　adjusted　according　to　the　extracted　key　factors.　Finally,　the　Gurobi　solver　in　Python　is　used　to　perform　numerical　experiments　on　the　classical　datasets　to　verify　the　effectiveness　of　the　instant　delivery　model.　The　proposed　AACO-RTLF　algorithm　shows　its　advantages　in　instant　delivery　order　scheduling　when　compared　to　the　other　state-of-the-art　algorithms.