Sentence semantic matching (SSM) is central to many natural language processing tasks. This is especially the case for Chinese sentence semantic matching due to the complexity of the semantics, missing semantics and semantic confusion are more likely to occur. Existing methods have used enhanced text representations and multiple matching strategies to address these problems but there is still great potential to capture deep semantic information for Chinese text. This paper proposes a Multi-Granularity and Internal-External correlation Residual model (MGIER) to better capture the deep semantic information and to alleviate the missing semantics effectively. First, the MGIER model utilizes character/word granularity to capture fine-grained information. Then, soft alignment attention is employed to enhance the correlation between characters/words in a sentence, called internal correlation, and the correlation between sentences, called external correlation. In particular, this method uses residual connections to preserve more semantic information from the bottom embedding layer to the top prediction layer. Experimental results show that the proposed method achieves state-of-the-art performance for Chinese SSM, and, compared with pre-trained models, the method also achieves better performance with fewer parameters.