Rapid　prediction　of　indoor　airborne　contaminant　distribution　is　crucial　for　controlling　indoor　air　quality.　Markov　chain　technology,　which　uses　matrix　multiplication,　is　computationally　efficient　in　this　field.　The　combination　of　computational　fluid　dynamics　(CFD)　with　Markov　chain　technology　has　gained　significant　attention　among　researchers.　The　partitioning　of　the　indoor　zone　directly　affects　the　size　and　values　of　the　transition　matrix,　which　is　the　core　of　the　Markov　chain　and　impacts　both　the　cost　and　accuracy　of　calculations.　Although　coarse　matrix　Markov　chain　models　incur　relatively　lower　computational　costs,　effective　methods　for　state　partitioning　are　lacking.　This　study　proposes　an　improved　method　for　non-uniform　state　partitioning　based　on　velocity　to　enhance　the　computational　accuracy　of　Markov　chain　models　with　coarse　matrices.　First,　we　used　the　airflow　velocity　field　obtained　by　CFD　simulation　to　divide　the　states　and　obtain　non-uniform　states.　Subsequently,　we　obtained　the　transition　matrix　corresponding　to　non-uniform　states　and　applied　it　to　the　Markov　chain　model　for　predictive　calculation.　We　compared　the　predictive　results　of　the　non-uniform　state-based　Markov　chain　model　with　experimental　and　CFD　simulation　data,　which　showed　that　the　model　had　a　good　predictive　performance　for　transient　contaminant　transport　and　that　the　non-uniform　state　partitioning　method　improved　predictive　accuracy.　Additionally,　we　discussed　the　effects　of　different　numbers　of　states　and　time　steps　on　computational　accuracy.　©　2023　Elsevier　Ltd