Recognizing textual entailment is intended to infer the logical relationship between two given sentences. In this paper

we incorporate the deep semantic information of sentences and the encoder of Transformer by constructing the SRL-Attention fusion module

and it effectively improves the ability of self-attention mechanism to capture sentence semantics. Furthermore

concerning the small scale and high noise problems on the dataset

we use large-scale pre-trained language model improving the recognition performance of model on small-scale dataset. Experimental results show that the accuracy of our model on the dataset CNLI

it is released as Chinese textual entailment recognition evaluation corpus at the 17th China National Conference on Computational Linguistics

reaches 80.28%.