Land cover mapping is an effective way to quantify land resources and monitor their changes. It plays an important role in a wide range of applications. This letter proposes a hybrid capsule network for land cover classification using multispectral light detection and ranging (LiDAR) data. First, the multispectral LiDAR data were rasterized into a set of feature images to exploit the geometrical and spectral properties of different types of land covers. Then, a hybrid capsule network composed of an encoder network and a decoder network is trained to extract both high-level local and global entity-oriented capsule features for accurate land cover classification. Quantitative classification evaluations on two data sets show that the overall accuracy, average accuracy, and kappa coefficient of over 97.89%, 94.54%, and 0.9713, respectively, are obtained. Comparative studies with five existing methods confirm that the proposed method performs robustly and accurately in land cover classification using the multispectral LiDAR data.