Zero-Shot classification aims at recognizing instances from unseen categories that have no training instances in the training stage. To address this task, most existing approaches resort to class semantic information to transfer knowledge from the seen classes to the unseen ones. In this paper, a transductive dictionary learning approach is proposed to facilitate the task in two steps. A discriminative dictionary learning model is first proposed for constructing the relations between the visual modality and the class semantic modality with the labeled seen instances. Then a transductive modified model is used to alleviate the domain shift issue caused by the disjointness between the seen classes and the unseen classes. Experimental results on three benchmark datasets (AwA, CUB and SUN) demonstrate the effectiveness and superiority of the proposed approach.