The spatial distribution of proteome at subcellular levels provides clues for protein functions, thus is important to human biology and disease. Imaging-based methods are one of the most important approaches for predicting protein subcellular location. Although deep neural networks (DNNs) have shown impressive performance in a number of imaging tasks, its application to protein subcellular localisation has not been sufficiently explored. In this study, we developed a deep imaging-based approach to localise the proteins at subcellular levels. Based on deep image features extracted from convolutional neural networks (CNNs), both single-label and multi-label locations can be accurately predicted. Particularly, the multi-label prediction is quite a challenging task. Here we developed a criterion learning strategy to exploit the labelattribute relevancy and label-label relevancy. A criterion which was used to determine the final label-set was automatically obtained during the learning procedure.We concluded an optimal CNN architecture that could give the best results. Besides, experiments show that compared with the hand-crafted features, the deep features present more accurate prediction with less features. The implementation for the proposed method is available at https://github.com/RanSuLab/ProteinSubcellularLocation.