Visual features used in state-of-the-art image clustering methods lack of independent learning ability, which leads to low image expression ability. Furthermore, the efficiency of traditional clustering methods is low for large image dataset. So, a fast image clustering method based on convolutional neural network and binary K-means is proposed in this paper. Firstly, a large-scale convolutional neural network is employed to learn the intrinsic implications of training images so as to improve the discrimination and representational power of visual features. Secondly, hashing is applied to map high-dimensional deep features into low-dimensional hamming space, and multi-index hash table is used to index the initial centers so that the nearest center lookup becomes extremely efficient. Finally, image clustering is accomplished efficiently by binary K-means algorithm. Experimental results on ImageNet-1000 dataset indicate that the proposed method can effectively enhance the expression ability of image features, increase the image clustering efficiency and has better performance than state-of-the-art methods.