A　CNN-LSTM　model-based　short　text　sentiment　classification　method　was　proposed　to　effectively　obtain　the　implicit　semantic　information　of　short　text　reviews.　The　convolutional　neural　network　(CNN)　model　was　used　to　automatically　learn　the　semantic　feature　by　setting　different　sizes　of　convolution　windows.　The　long　short-term　memory　(LSTM)　neural　network　model　was　used　to　predict　the　sentimental　label　of　the　short　text.　The　performance　of　the　model　was　evaluated　on　three　different　short　text　review　data　sets.　The　F1　value　of　the　positive　and　negative　data　in　NLPCC　is　0.768　3　and　0.772　4,　respectively　(better　than　the　best　NLPCC　evaluation　result).　Compared　with　the　traditional　machine　learning　classification　model,　t-test　results　show　that　the　performance　is　improved　significantly.　©　2019,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.