Abstract: In case based machine translation, the bilingual case of sentence is always used. The utilization of sentence case with big granularity is low, because of unlimited multiplicity in natural language. In rule based machine translation, the knowledge of rule and dictionary is abstract, and their usage is frequent, but there is much ambiguity. Proposed in this paper is an automatic optimization algorithm of case in IHSMT. The algorithm defines the concept of case granularity and decomposes the big case granularity into small ones, and then the related dictionary is built, so the frequency of utility and efficiency of retrieval are improved. At the same time, the small granularity with ambiguity can be combined according to the modification of users and the information of context in translation, so the quality of translation and efficiency of reasoning are better. The case optimization makes the granularity and the storage of case more reasonable, and the efficiency of the system is improved.