A semi-supervised graph-based clustering method is presented with composite kernel for the hyperspectral images, mainly to solve the problems existed in an algorithm called Semi-Supervised Graph-Based Clustering (SSGC) and improve its performance. As for the realization, it firstly reforms the Radial Basis Function (RBF) by adopting semi-supervised approach, to exploit the wealth of unlabeled samples in the image. Then, it incorporates the spectral angle kernel with RBF kernel, and constructs a composite kernel. At last, the use of K-Nearest Neighbor (KNN) method while constructing the weight matrix has greatly simplified the calculation. Experimental result in Indian Pine and Botswana hyperspectral data demonstrates that this algorithm can not only get higher classification accuracy (1%～4% higher than SSGC, 10%～20% higher than K-means and Fuzzy C-Means (FCM), but effectively improve operation speed compared with SSGC.