The　COVID-19　pandemic　has　significantly　impacted　people＇s　daily　lives　for　over　three　years.　Airports,　with　their　dense　population　and　frequent　close　contact,　pose　a　higher　risk　of　respiratory　infectious　diseases　compared　to　many　other　indoor　environments.　However,　limited　availability　of　data　on　close　contact　behavior　has　resulted　in　a　gap　in　indoor　exposure　analysis.　This　study　conducted　depth　sensor　measurements　and　video　data　collection　across　nine　areas　of　a　northern　(airport　A)　and　a　southern　(airport　B)　airports　in　China　by　11　participants.　The　data,　comprising　more　than　44　h　of　close　contact　behaviors,　including　interpersonal　distance,　relative　facial　orientation,　and　the　relative　position　of　individuals,　were　analyzed　using　a　semi-supervised　ma-chine　learning　method.　Based　on　this　analysis,　a　close　contact　transmission　model　for　COVID-19　was　developed,　which　considers　the　aforementioned　close　contact　behaviors　to　assess　the　risk　of　exposure　and　the　efficacy　of　interventions.　The　average　close　contact　ratio　in　9　airport＇s　areas　is　25.4　%　(ranging　from　6.1　%　to　55.0　%),　with　passengers　having　the　highest　frequency　of　close　contact　in　manual　check-in　areas.　During　close　contacts,　the　average　interpersonal　distance　in　airports　is　1.2　m　(ranging　from　1.1　to　1.4　m),　being　shortest　in　boarding　areas.　Face-to-face　close　contact　is　highest　in　charging　areas,　with　a　percentage　of　46.9　%.　If　people　maintain　a　distance　of　over　1.0　m　in　all　areas,　the　total　virus　exposure　could　be　reduced　by　6.9　%-22.0　%　compared　to　the　actual　situation.　Dining　areas　have　the　highest　virus　exposure　risk　for　both　short-range　inhalation　and　mucosal　deposition,　followed　by　manual　check-in　areas.　This　study　provides　a　data　support　for　the　scientific　epidemic　prevention　and　control　in　airports　from　the　viewpoint　of　close　contact　behaviors.