As　the　popularity　of　fresh　air　systems　(FAS)　in　residential　buildings　increases,　exploring　the　behavioral　characteristics　of　their　use　can　help　to　provide　a　comprehensive　understanding　of　the　potential　for　demand　flexibility　in　residential　buildings.　However,　few　studies　in　the　past　have　focused　on　the　personalized　usage　behavior　of　FAS.　To　fill　this　gap,　this　study　proposes　a　method　based　on　data　mining　techniques　to　reveal　the　behavioral　patterns　of　FAS　usage　and　the　motivations　behind　them,　including　motivational　patterns,　operation　duration　patterns,　and　human-machine　interaction　patterns,　for　13　households　in　Beijing.　The　simultaneously　obtained　behavioral　patterns,　in　turn,　form　the　basis　of　association　rules,　which　can　classify　FAS　usage　behavior　into　two　typical　residential　user　profiles　containing　user　behavioral　characteristics.　This　study　can　not　only　provide　more　accurate　assumptions　and　inputs　for　behavioral　stochastic　models　but　also　provide　data　support　for　the　development　and　optimization　of　demand　response　strategies.