The invention discloses a zero-sample deep-sea biological picture classification method based on multiple classifiers. The method comprises the following steps that (1) image feature data importing, wherein a data set is composed of visual features, semantics and label information of visible categories and invisible categories, common marine organism categories with labels serve as the visible categories, and deep-sea organism categories lack of data serve as the invisible categories; (2) class specific classifier training: utilizing visible class data training to establish different visual semantic mappings for different classes on the basis of reserving a manifold structure so as to improve the classification accuracy of the visible classes; and (3) inferring the invisible category, migrating the weight between the visible category and the invisible category semantics, synthesizing a mapping matrix of the invisible category by using the mapping matrix of the visible category, and obtaining the label of the invisible category according to the distance. According to the method, the influence of mapping domain drift is reduced to a certain extent, and the method is accurate and feasible.