Because of ground observation instruments and other factors, we can not recognize the space target only from the external shape in the image. Since the reflection spectrum of the space target is determined by the surface material of space object, spectral analysis technique can be used for classifying the space objects. Based on the K-nearest neighbor algorithm (KNN), a method called adaptive weight k-local hyperplane (AWKH) is proposed in this paper. The main improvement of the algorithm is that weight discrimination is added in the processes of calculating the hyperplane distance between predicted samples. The algorithm constructs a hyperplane model by using the difference between the groups and within group ratio for the weights of features. In order to verify the classification effectiveness and efficiency of the algorithm, this paper carried out four sets of verification experiments. In the first set of experiments, 9 kinds of common materials were extracted from the database of United State Geological Survey. Then 3 kinds of these materials were mixed into multi-class objections. In the second and third sets of experiments, the spectra of four normal space target materials were mixed in different classes. Then these classes were identified from the visible and near-infrared wave bands. In the fourth set of experiments, four square models of hexahedron were classified by the spectra of their surface material. The experimental results indicate that the AWKH algorithm has more advantages in identification accuracy and effectiveness of the complex samples by comparing with the support vector machine (SVM) method.