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The computer-aided diagnosis for histopathological images has attracted considerable attention. Principal component analysis network (PCANet) is a novel deep learning algorithm for feature learning with the simple network architecture and parameters. In this study, a color pattern random binary hashing-based PCANet (C-RBH-PCANet) algorithm is proposed to learn an effective feature representation from color histopathological images. The color norm pattern and angular pattern are extracted from the principal component images of R, G, and B color channels after cascaded PCA networks. The random binary encoding is then performed on both color norm pattern images and angular pattern images to generate multiple binary images. Moreover, we rearrange the pooled local histogram features by spatial pyramid pooling to a matrix-form for reducing the dimension of feature and preserving spatial information …