Concerning the problems of poor classification of Capsule Network （CapsNet） on complex datasets and large number of parameters in the routing process， a Capsule Network based on Multipath feature （MCNet） was proposed， including a novel capsule feature extractor and a novel capsule pooling method. By the capsule feature extractor， the features of different layers and locations were extracted in parallel from multiple paths， and then the features were encoded into capsule features containing more semantic information. In the capsule pooling method， the most active capsules at each position of the capsule feature map were selected， and the effective capsule features were represented by a small number of capsules. Comparisons were performed on four datasets （CIFAR-10， SVHN， Fashion-MNIST， MNIST） with models such as CapsNet. Experimental results show that MCNet has the classification accuracy of 79.27% on CIFAR-10 dataset and the number of trainable parameters of 6.25×10⁶； compared with CapsNet， MCNet has the classification accuracy improved by 8.7%， and the number of parameters reduced by 46.8%. MCNet can effectively improve the classification accuracy while reducing the number of trainable parameters.