Due　to　the　randomness　and　uncertainty　in　the　atmospheric　environment,　and　accompanied　by　a　variety　of　unknown　noise.　Accurate　prediction　of　PM2.5　concentration　is　very　important　for　people　to　prevent　injury　effectively.　In　order　to　predict　PM2.5　concentration　more　accurately　in　this　environment,　a　hybrid　modelling　method　of　support　vector　regression　and　adaptive　unscented　Kalman　filter　(SVR-AUKF)　is　proposed　to　predict　atmospheric　PM2.5　concentration　in　the　case　of　incorrect　or　unknown　noise.　Firstly,　the　PM2.5　concentration　prediction　model　was　established　by　support　vector　regression.　Secondly,　the　state　space　framework　of　the　model　is　combined　with　the　adaptive　unscented　Kalman　filter　method　to　estimate　the　uncertain　PM2.5　concentration　state　and　noise　through　continuous　updating　when　the　model　noise　is　incorrect　or　unknown.　Finally,　the　proposed　method　is　compared　with　SVR-UKF　method,　the　simulation　results　show　that　the　proposed　method　is　more　accurate　and　robust.　The　proposed　method　is　compared　with　SVR-UKF,　AR-Kalman,　AR　and　BP　methods.　The　simulation　results　show　that　the　proposed　method　has　higher　prediction　accuracy　of　PM2.5　concentration.