A statistical method based on information entropy is proposed for domain-specific term extraction from domain comparative corpora.It takes into account the distribution of a candidate word among domains and within a certain domain.Normalization step is added into the extraction process to cope with unbalanced corpora.The proposed method characterizes attributes of domain-specific term more precisely and more effectively than previous term extraction approaches.Domain-specific terms are applied in text classification as the feature space.Experimental results indicate that it achieves better performance than traditional feature selection methods.