Hyperspectral images (HSIs) are obtained from hyperspectral imaging sensors, which capture information in hundreds of spectral bands of objects. However, how to take full advantage of spatial and spectral information from many spectral bands to improve the performance of HSI classification remains an open question. Many HSI classification works have recently been reported by employing multiview learning (MVL) algorithms that can fully use complementary information between different view features and thus have received widespread attention. This letter proposes a multiview fusion network based on multiorder interaction information embedding for HSI classification. First, the correlation matrix between spectral bands is used to divide the original data into multiple subsets as local views. The subset after the segmented-PCA process is used as the global view. Second, the features of different views are extracted separately using a feature extraction network and mapped to the same dimension. Prefusion is achieved by multiorder interaction of various view features. Finally, loss-weighted fusion is applied to each view according to its contribution to the classification task. To evaluate the effectiveness of the proposed method, complete experiments were conducted on three commonly used HSI datasets, namely Pavia University, Houston 2013, and Houston 2018. The experimental results demonstrate that the proposed method improves the classification performance of existing feature extraction networks and is more competitive with other methods in the field.