Abstract: Classification that assigns label for pixel in HSI dataset is an important pre-processed method in hyperspectral image (HSI) processing, label information is useful for application such as recognition and exploration. A graph based semi-supervised classification method is proposed to tackle problems of large data volume, high data dimension, and small known sample size in HSI classification task. Dataset was modeled with graph for dimensional reducing, then the task is formulated as an unconstrained optimization problem in this method. Matrix inverse is inevitable for solving such problem, and complexity would increase with large scale. In order to avoid large scale matrix inversion, a quasi-Newton method which approximates inversion operation according to decomposition of Hessian matrix is used, such method can be implemented in distributed manner. Simulations demonstrate that, compared with existing methods, proposed algorithm has lower complexity and higher accuracy in large scale and multi-class HSI classification task.