In the practical applications of image classification,some categories may have no labeled training data at all. The purpose of Zero-Shot Learning(ZSL)is to transfer knowledge such as image features of labeled categories to unlabeled categories and to correctly classify the unlabeled categories. However,the existing state-of-the-art methods cannot explicitly distinguish the input image belonging to the known categories or unknown categories,which leads to a large performance gap for unlabeled categories between the traditional ZSL prediction and the Generalized ZSL(GZSL)prediction. Therefore,a method of fusing of visual error and semantic attributes was proposed to alleviate the prediction bias problem in zero-shot image classification. Firstly,a semi-supervised learning based generative adversarial network framework was designed to obtain visual error information,so as to predict whether the image belongs to the known categories. Then,a zero-shot image classification network combining semantic attributes was proposed to achieve zero-shot image classification. Finally,the performance of zero-shot image classification algorithm combining visual error and semantic attributes was tested on AwA2 (Animal with Attributes) and CUB (Caltech-UCSD-Birds-200-2011) datasets. The experimental results show that, compared to the baseline models,the proposed method can effectively alleviate the prediction bias problem,and has the harmonic index H increased by 31. 7 percentage points on AwA2 dataset and 8. 7 percentage points on CUB dataset.