The　classification　of　Chinese　short　text　suffers　from　the　problem　of　feature　sparsity.　In　this　paper,　we　propose　a　Chinese　short　text　classification　method　based　on　Bert　sentence　embedding　integrating　external　statistical　features　(CSTCBERTSE-ESF).　[Methods]　The　CSTC-BERTSE-ESF　method　uses　BERT　sentence　embedding　as　the　feature　base,　then　concatenate　domain　features　obtained　from　the　domain　corpus　by　statistical　methods　as　the　complete　features　of　the　classifier,　and　finally　uses　Random　Forest　and　Softmax　for　the　classification.　[Results]　To　verify　the　effectiveness　of　the　method,　a　series　of　experiments　were　conducted　in　this　paper　using　THUNews　with　the　Book　dataset.　The　accuracy　of　the　method　reached　93.8%　and　93%　on　the　two　datasets,　respectively,　and　the　F1　value　of　CSTC-BERTSE-ESF　can　be　improved　by　8.8%　compared　to　CSTC-BERTSE　on　the　book　dataset.　[Limitation]　Due　to　the　limitation　of　computing　power,　the　domain　corpora　used　in　this　paper　are　small,　and　the　domain　features　will　be　more　accurate　if　the　capacity　of　the　corpus　is　increased.　[Conclusion]　The　fusion　of　external　domain　features　can　effectively　improve　the　classification　of　Chinese　short　texts,　especially　the　data　with　strong　domain　knowledge.　©　2022　IEEE.