Recognizing textual entailment (RTE) is a task to recognize whether two sentences have an entailment relationship. In recent years, RTE in English had made a great progress. The current researches are mainly based on type judgment, and pay less attention to locate the language chunks that lead to the entailment relationship. More over, it leads to a low interpretability of the RTE models. This study selects 12 000 Chinese entailment sentence pairs from the Chinese Natural Language Inference (CNLI) data and labeled chunks which lead to their entailment relationship. Then 7 entailment types are summarized considering Chinese linguistic features. On the basis, two tasks are proposed. One is to recognize the seven-category of entailment type for each entailment sentence pairs, another is to recognize the boundaries of the entailment chunks in it. The proposed deep learning based method reaches an accuracy of 69.19% and 62.09% in the two tasks. The experimental results show that proposed approaches can effectively identifying different types of entailment in Chinese and find the boundaries of the entailment chunks, which demonstrate that the proposed model provides a reliable benchmark for further research.