Document (text) classification is a common method in e-business, facilitating users in tasks such as document collection, analysis, categorization and storage. However, few previous methods consider the classification tasks from the perspective of semantic analysis. This paper proposes two novel semantic document classification strategies to resolve two types of semantic problems: (1) polysemy problem, by using a novel semantic similarity computing strategy (SSC) and (2) synonym problem, by proposing a novel strong correlation analysis method (SCM). Experiments show that the proposed strategies improve the performance of document classification compared with that of traditional approaches.