Support　vector　machine　(SVM)　has　achieved　excellent　results　in　short　text　classification.　However,　its　performance　is　limited　in　the　kernel　function.　This　paper　presents　a　short　text　classification　method　based　on　Cross-connected　GRU　Kernel　Mapping　Support　Vector　Machine　(C-GRUKMSVM),　to　further　improve　the　accuracy　of　short　text　classification.　The　method　consists　of　a　feature　mapping　module　and　a　classification　module.　The　feature　mapping　module　first　represents　the　text　as　a　word　vector　using　the　glove　method,　and　then　explicitly　maps　the　low-dimensional　word　vector　to　a　high-dimensional　space　using　a　three-layer　cross-connected　GRU;　the　classification　module　uses　a　soft-margin　support　vector　machine　for　classification.　Experimental　results　on　five　publicly　available　short　text　datasets　show　that　C-GRUKMSVM　achieves　better　text　classification　performance　than　convolutional　networks,　support　vector　machines　and　Naïve　Bayes.　Additionally,　different　cross-connected　methods,　recurrent　units　and　recurrent　structures　have　an　impact　on　the　performance　of　C-GRUKMSVM.　©　2021　IEEE.