Urbanization,　industrialization,　and　regional　economic　integration　have　developed　rapidly　in　China　in　recent　years.　Air　pollution　has　attracted　more　and　more　attention.　However,　PM2.5　is　the　main　particulate　matter　in　air　pollution.　Therefore,　how　to　predict　PM2.5　accurately　and　effectively　has　become　a　concern　of　experts　and　scholars.　For　the　problem,　atmosphere　PM2.5　concentration　prediction　algorithm　is　proposed　based　on　time　series　and　interactive　multiple　model　in　this　paper.　PM2.5　concentration　is　collected　by　using　the　monitor　at　different　air　quality　levels.　The　time　series　models　are　established　by　historical　PM2.5　concentration　data,　which　were　given　by　the　autoregressive　model　(AR).　In　the　paper,　three　PM2.5　time　series　models　are　established　for　three　different　air　quality　levels.　Then,　the　three　models　are　converted　to　state　equation,　respectively,　by　autoregressive　integrated　with　Kalman　filter　(AR-Kalman)　approaches.　Besides,　the　proposed　interactive　multiple　model　(IMM)　algorithm　is,　respectively,　compared　with　autoregressive　(AR)　model　algorithm　and　AR-Kalman　prediction　algorithm.　It　is　turned　out　the　proposed　IMM　algorithm　is　more　accurate　than　the　other　two　approaches　for　PM2.5　prediction,　and　it　is　effective.