Zero-shot learning aims to recognize the unseen classes by using the knowledge of the seen classes that has been learned. In recent years, ‘knowledge+data driven’ has become a new trend but lacking of unified definition of ‘knowledge’ in the current zero-shot tasks of computer vision. This study tries to define the ‘knowledge’ in this field and divided it into three categories, which are primary knowledge, abstract knowledge, and external knowledge. In addition, based on the definition and classification of knowledge, the current works on zero-shot learning (mainly in image classification task) are sorted out, they are divided into zero-shot models based on primary knowledge, zero-shot models based on abstract knowledge, and zero-shot models based on external knowledge. This study also introduces the problems which are domain shift and hubness in this field, and further summarizes existing works based on the problems. Finally, the paper summarizes the datasets and knowledge bases that commonly used in image classification tasks, the evaluation criteria of image classification experiment and the experimental results of representative models. The future works are also summarized and prospected.