Capsule network is a novel architecture to encode the properties and spatial relationships of the feature in the images, which shows encouraging results on image classification. However, the original capsule network is not suitable for some classification tasks that the detected object has complex internal representations. Hence, we propose Multi-Scale Capsule Network, a novel variation of capsule network to enhance the computational efficiency and representation capacity of capsule network. The proposed Multi-Scale Capsule Network consists of two stages. In the first stage the structural and semantic information are obtained by the multi-scale feature extraction. The second stage, we encode the hierarchy of features to multi-dimensional primary capsule. Moreover, we propose an improved dropout to enhance the robustness of capsule network. Experimental results show that our method has competitive performance on FashionMNIST and CIFAR10 datasets.