Automatic medical code assignment for clinical records is the fundamental problem of medical statistical research and informatization. Due to the high dimension and sparse distribution of label space, it is necessary to make full use of the description information of the labels. However, most of the current work is based on similarity matching at the level of token or n-gram, and ignores information fusion with richer semantic structure and representation. In this paper, we propose a Clause Interaction HyperGraph (CIHG) to jointly model documents and label descriptions, which construct a richer semantic interaction at the level of clause. The CIHG models the high-order co-occurrence relationship between document and labels based on hypergraph, and uses the semantic structure of the document to constrain the encoding of labels. Experiments on widely used medical code assignment datasets show that our method successfully constrains the embedding of labels and significantly improves predictive precision.