The　particulate　matter　(PM)　concentration　in　subway　platforms　greatly　affects　people＇s　health.　Previous　control　strategies　fail　to　consider　comprehensive　factors,　or　exhibit　deficient　in　quantitative　suggestions.　In　this　paper,　we　propose　a　comprehensive　framework　for　exploring　control　strategies　of　the　PM　concentration　in　subway　platforms　using　the　field　observation　data　of　12　subway　stations　of　Beijing.　The　framework　consists　of　factor　analysis　stage,　simulation　stage,　and　prediction　stage.　The　first　stage　extracts　five　important　factors　(concentration　of　outdoor　PM,　passenger　flow,　train　departure　frequency,　operation　years　and　platform　type)　related　to　the　PM　concentration　in　platforms,　and　ranks　the　importance　of　those　factors.　The　second　stage　reveals　two　vertical　distribution　laws　and　a　horizontal　distribution　law　of　the　PM　concentration　in　platforms.　The　third　stage　predicts　the　PM2.5　concentration　in　platforms　based　on　the　mass　balance　model.　Finally,　we　suggest　seven　pieces　of　control　strategies　including　regularly　clean　tunnels,　platforms　and　ventilation　systems;　release　negative　ionic　air;　and　add　air　cleaners　in　specific　locations.　In　particular,　we　suggest　a　quantitative　strategy　on　the　wind　speed　of　air　supply　outlet　which　can　reduce　the　PM2.5　concentration　in　platforms　15.4%-　62.3%　under　different　air　quality.　The　results　of　our　study　can　provide　a　scientific　basis　for　control　of　the　particulate　pollution　in　platforms,　and　effectively　improve　the　subway　environment.