A zero-shot image classification method based on deep supervised alignment network (DSAN) was proposed to address the problems caused by poor class discrimination of attributes and the bias of classifying images into seen classes in generalized zero-shot image classification. The global supervised tags were constructed and used along with the attribute vectors annotated by expert systems to enhance the discrimination ability of class semantics. To align the manifolds structure of visual and semantic space, image and semantic feature classification networks were designed to learn their class distributions respectively, which were aligned afterwards with no difference. The generative adversarial network was also utilized to eliminate the heterogeneity between them. The element-wise addition was used to merge visual features and class semantic features when learning their nonlinear similarity by relation network. Experimental results showed that the harmonic mean classification accuracy for seen and unseen classes of the proposed method outperformed the baseline model by 4.3%, 19.5% and 21.9% on CUB, AWA1, AWA2 datasets, respectively. The harmonic mean classification accuracy was 1.4% and 2.2% higher than those of the existing best-performing CRnet method on SUN and APY datasets, respectively. The results demonstrated the effectiveness of the proposed method.