The　PM2.5　concentration　model　is　the　key　to　predict　PM2.5　concentration.　During　the　prediction　of　atmospheric　PM2.5　concentration　based　on　prediction　model,　the　prediction　model　of　PM2.5　concentration　cannot　be　usually　accurately　described.　For　the　PM2.5　concentration　model　in　the　same　period,　the　dynamic　characteristics　of　the　model　will　change　under　the　influence　of　many　factors.　Similarly,　for　different　time　periods,　the　corresponding　models　of　PM2.5　concentration　may　be　different,　and　the　single　model　cannot　play　the　corresponding　ability　to　predict　PM2.5　concentration.　The　single　model　leads　to　the　decline　of　prediction　accuracy.　To　improve　the　accuracy　of　PM2.5　concentration　prediction　in　this　solution,　a　multiple　model　adaptive　unscented　Kalman　filter　(MMAUKF)　method　is　proposed　in　this　paper.　Firstly,　the　PM2.5　concentration　data　in　three　time　periods　of　the　day　are　taken　as　the　research　object,　the　nonlinear　state　space　model　frame　of　a　support　vector　regression　(SVR)　method　is　established.　Secondly,　the　frame　of　the　SVR　model　in　three　time　periods　is　combined　with　an　adaptive　unscented　Kalman　filter　(AUKF)　to　predict　PM2.5　concentration　in　the　next　hour,　respectively.　Then,　the　predicted　value　of　three　time　periods　is　fused　into　the　final　predicted　PM2.5　concentration　by　Bayesian　weighting　method.　Finally,　the　proposed　method　is　compared　with　the　single　support　vector　regression-adaptive　unscented　Kalman　filter　(SVR-AUKF),　autoregressive　model-Kalman　(AR-Kalman),　autoregressive　model　(AR)　and　back　propagation　neural　network　(BP).　The　prediction　results　show　that　the　accuracy　of　PM2.5　concentration　prediction　is　improved　in　whole　time　period.