Predicting　transient　particle　transport　is　crucial　to　address　the　risks　posed　to　human　health　by　indoor　contaminants　and　to　improve　the　design　and　control　of　ventilation　systems.　The　Markov　chain　model　with　a　coarse　matrix　has　proven　to　be　efficient　in　this　regard,　demonstrating　faster　speeds　than　traditional　methods　and　higher　computational　accuracy　when　based　on　non-uniform　states.　However,　its　application　under　unsteady　airflow　conditions　is　limited,　and　discussions　of　how　to　determine　non-uniform　Markov　states　and　their　corresponding　transient　transition　probabilities　when　the　flow　field　changes　are　limited.　Therefore,　this　study　developed　an　application　method　of　Markov　chain　model　based　on　fixed　non-uniform　states　in　unsteady　airflow　fields.　First,　this　model　rapidly　provides　various　Markov　state　schemes　based　on　airflow　field　velocity　and　clustering　methods.　It　then　selects　the　optimal　scheme　through　frequency　and　finally　obtains　the　transition　matrix　through　Lagrangian　tracking　for　particle　transport　prediction.　This　model　reduces　the　computational　cost　of　transient　particle　transport　when　the　wind　speed　changes　while　ensuring　high　computational　accuracy.　The　proposed　method　was　validated　using　published　flow　field　experiments　and　simulation　data.　The　computational　speed　of　the　proposed　method　was　compared　with　computational　fluid　dynamics　(CFD),　and　in　the　case　studies　considered　in　this　research,　the　normalized　root-mean-square　deviation　of　the　model　was　less　than　10%　and　the　total　computation　time　was　more　than　75%　faster　than　that　of　CFD.