Window　operation　is　an　important　occupant　behavior,　and　has　significant　impacts　on　building　energy　consumption.　Recently,　various　stochastic　and　non-stochastic　models　have　been　proposed,　aiming　to　describe　occupant　window　behavior　based　on　several　influencing　factors.　However,　most　of　the　employed　methods　are　logit　regression　and　Markov　chain　techniques,　and　the　application　of　machine　learning　to　model　occupants＇　window　behavior　is　rarely　investigated.　In　addition,　most　published　studies　referring　to　occupants＇　window　behavior　have　been　carried　out　within　European　countries,　where　the　influence　of　outdoor　air　quality　is　rarely　considered.　This　study　compares　different　models　of　occupants＇　window　behavior,　including　models　based　on　logistic　regression,　Markov　processes,　and　an　artificial　neural　network.　An　artificial　neural　network　model　is　proposed　to　explore　the　application　and　optimization　of　an　artificial　neural　network　algorithm　under　a　condition　of　having　less　samples.　Moreover,　the　outdoor　fine　inhalable　particles　(PM2.5)　concentration　is　considered　as　an　influencing　factor　for　building　a　window　opening　model　for　office　buildings　during　the　transition　season　in　China.　From　this　work,　it　is　generally　concluded　that　the　PM2.5　concentration　and　outdoor　humidity　should　be　considered　in　the　modeling　of　occupant　window　behavior　in　Beijing,　China.　In　addition,　more　true　estimations　can　be　obtained　from　artificial　neural　network　models　than　from　logistic　regression　models　and　Markov　models.　This　result　demonstrates　that　the　proposed　artificial　neural　network　yields　a　prediction　model　of　office　window　states　with　higher　accuracy　and　better　interpretability　of　highly　correlated　factors　as　compared　to　logistic　regression　models　and　Markov　models.　The　proposed　approaches　provide　a　new　and　detailed　way　for　engineers　and　building　operators　to　better　understand　occupant　window　behaviors　and　their　impacts　on　energy　use　in　office　buildings.