Task　allocation　is　a　critical　issue　of　spatial　crowdsourcing.　Although　the　batching　strategy　performs　better　than　the　real-time　matching　mode,　it　still　has　the　following　two　drawbacks:　(1)　Because　the　granularity　of　the　batch　size　set　obtained　by　batching　is　too　coarse,　it　will　result　in　poor　matching　accuracy.　However,　roughly　designing　the　batch　size　for　all　possible　delays　will　result　in　a　large　computational　overhead.　(2)　Ignoring　non-stationary　factors　will　lead　to　a　change　in　optimal　batch　size　that　cannot　be　found　as　soon　as　possible.　Therefore,　this　paper　proposes　a　fine-grained,　batching-based　task　allocation　algorithm　(FGBTA),　considering　non-stationary　setting.　In　the　batch　method,　the　algorithm　first　uses　variable　step　size　to　allow　for　fine-grained　exploration　within　the　predicted　value　given　by　the　multi-armed　bandit　(MAB)　algorithm　and　uses　the　results　of　pseudo-matching　to　calculate　the　batch　utility.　Then,　the　batch　size　with　higher　utility　is　selected,　and　the　exact　maximum　weight　matching　algorithm　is　used　to　obtain　the　allocation　result　within　the　batch.　In　order　to　cope　with　the　non-stationary　changes,　we　use　the　sliding　window　(SW)　method　to　retain　the　latest　batch　utility　and　discard　the　historical　information　that　is　too　far　away,　so　as　to　finally　achieve　refined　batching　and　adapt　to　temporal　changes.　In　addition,　we　also　take　into　account　the　benefits　of　requesters,　workers,　and　the　platform.　Experiments　on　real　data　and　synthetic　data　show　that　this　method　can　accomplish　the　task　assignment　of　spatial　crowdsourcing　effectively　and　can　adapt　to　the　non-stationary　setting　as　soon　as　possible.　This　paper　mainly　focuses　on　the　spatial　crowdsourcing　task　of　ride-hailing.