With　the　advancement　of　the　Internet,　social　media　platforms　have　gradually　become　powerful　in　spreading　crisis-related　content.　Identifying　informative　tweets　associated　with　natural　disasters　is　beneficial　for　the　rescue　operation.　When　faced　with　massive　text　data,　choosing　the　pivotal　features,　reducing　the　calculation　expense,　and　increasing　the　model　classification　performance　is　a　significant　challenge.　Therefore,　this　study　proposes　a　multi-strategy　improved　black-winged　kite　algorithm　(MSBKA)　for　feature　selection　of　natural　disaster　tweets　classification　based　on　the　wrapper　method＇s　principle.　Firstly,　BKA　is　improved　by　utilizing　the　enhanced　Circle　mapping,　integrating　the　hierarchical　reverse　learning,　and　introducing　the　Nelder-Mead　method.　Then,　MSBKA　is　combined　with　the　excellent　classifier　SVM　(RBF　kernel　function)　to　construct　a　hybrid　model.　Finally,　the　MSBKA-SVM　model　performs　feature　selection　and　tweet　classification　tasks.　The　empirical　analysis　of　the　data　from　four　natural　disasters　shows　that　the　proposed　model　has　achieved　an　accuracy　of　0.8822.　Compared　with　GA,　PSO,　SSA,　and　BKA,　the　accuracy　is　increased　by　4.34%,　2.13%,　2.94%,　and　6.35%,　respectively.　This　research　proves　that　the　MSBKA-SVM　model　can　play　a　supporting　role　in　reducing　disaster　risk.