Deep learning has emerged as a cutting-edge approach for land cover classification using hyperspectral remote sensing images. However, hyperspectral remote sensing images suffer from two inherent limitations: spectral similarity between different objects and the absence of ground height information. To address these issues, this study proposes a multimodal land cover classification algorithm based on cross-feature granularity fusion network (CFCGNet), which establishes feature fusion between LiDAR data and hyperspectral remote sensing images. CFCGNet incorporates a multimodal cross-feature fusion module designed to extract high-order semantic features and complementary information from multisource data. Moreover, a composite granularity feature integration module is designed to integrate multiscale semantic information from various data sources. To mitigate the issue of uneven distribution of land use type samples, a weighted loss function is developed to optimize the model training process. Experimental results on the publicly available MUUFL, Houston 2018, and Trento datasets demonstrate that the proposed CFCGNet algorithm improves classification efficiency and enhances theoretical interpretability.