Semantic entailment recognition aims to detect and judge whether the semantics of two Chinese sentences are consistent and whether there is an entailment relationship. The existing methods, however, usually face the challenges of Chinese synonyms, polysemy, and difficulty in understanding long texts. To solve the above problems, this study proposes a co-driven Chinese semantic entailment recognition method based on the fusion of Transformer and sememe knowledge of HowNet. First, the internal structural semantic information of Chinese sentences is encoded at multiple levels and undergoes data-driven processing by Transformer. The external knowledge base HowNet is introduced for knowledge-driven modeling of the sememe knowledge correlations between words. Then, the interaction attention is calculated by Soft-Attention and achieves knowledge fusion with the sememe matrix. Finally, BiLSTM is used to encode the semantic information of the conceptual layer of texts and infer and judge the semantic consistency and entailment relationship. The proposed method employs the sememe knowledge of HowNet to solve the problems of polysemy and synonyms and uses the Transformer strategy to resolve the challenge of long texts. The experimental results on financial and multi-semantic interpretation pair data sets such as BQ, AFQMC, and PAWSX show that compared with lightweight models such as DSSM, MwAN, and DRCN and pre-trained models such as ERNIE, this model can effectively improve the recognition accuracy of Chinese semantic entailment (an increase of 2.19% compared with that of the DSSM model) and control the number of model parameters (16 M). In addition, it can also adapt to entailment recognition scenarios of long texts with no less than 50 words.