The hyperspectral image contains rich spectral and spatial features, which are essential for the classification of surface materials. However, the spatial resolution of hyperspectral images is relatively low, resulting in a large number of mixed pixels in the image, which severely restricts the accuracy of substance classification. Affected by factors such as observation noise, target area size, and endmember variability, hyperspectral image classification still faces many challenges. With the continuous progress of artificial intelligence and information processing technology, hyperspectral image classification has become a hot issue in the field of remote sensing. First, the literature on hyperspectral image classification based on feature fusion is systematically reviewed, and several classification strategies are analyzed and compared. Then, the development status of hyperspectral image classification and the corresponding problems are introduced. Finally, some suggestions can improve the classification performance are proposed, which provide guidance and assistance for the technical research of the subject.