This　paper　mainly　deals　with　the　problem　of　short　text　classification.　There　are　two　main　contributions.　Firstly,　we　introduce　a　framework　of　deep　uniform　kernel　mapping　support　vector　machine　(DUKMSVM).　The　significant　merit　of　this　framework　is　that　by　expressing　the　kernel　mapping　function　explicitly　with　a　deep　neural　network,　it　is　in　essence　an　explicit　kernel　mapping　instead　of　the　traditional　kernel　function,　and　it　allows　better　flexibility　in　dealing　with　various　applications　by　applying　different　neural　network　structures.　Secondly,　to　validate　the　effectiveness　of　this　framework　and　to　improve　the　performance　of　short　text　classification,　we　explicitly　express　the　kernel　mapping　using　bidirectional　recurrent　neural　network　(BRNN),　and　propose　a　deep　bidirectional　recurrent　kernel　mapping　support　vector　machine　(DRKMSVM)　for　short　text　classification.　Experimental　results　on　five　public　short　text　classification　datasets　indicate　that　in　terms　of　classification　accuracy,　precision,　recall　rate　and　F1-score,　the　DRKMSVM　achieves　the　best　performance　with　the　average　values　of　accuracy,　precision,　recall　rate,　and　F1-score　of　87.23%,　86.99%,　86.13%　and　86.51%　respectively　compared　to　traditional　SVM,　convolutional　neural　network　(CNN),　Naive　Bayes　(NB),　and　Deep　Neural　Mapping　Support　Vector　Machine　(DNMSVM)　which　applies　multi-layer　perceptron　for　kernel　mapping.