The invention provides a robust structure non-negative matrix factorization clustering method based on graph regularization, and the method comprises the steps: S10, obtaining m to-be-clustered images, and constructing k nearest neighbor graphs according to the to-be-clustered images; S20, a corresponding data matrix Y is obtained for each nearest neighbor graph, the data matrix Y comprises n datapoints, and a non-negative matrix decomposition method is used for decomposing the data matrix Y to obtain a feature matrix W and a coefficient matrix H; S30, establishing an objective function O ofrobust structure non-negative matrix decomposition based on graph regularization based on l2 and p norms; S40, according to the objective function O, using an iterative weighting method to iterate preset times, and updating the feature matrix W, the coefficient item and the graph regular item; S50 analyzing and clustering the feature matrix W obtained by each nearest neighbor graph by using a k-means clustering algorithm. According to the method, a robust loss function is adopted to measure a reconstruction error therein, mark data is not used in the robust loss function for judgment, and after a semi-supervised method of non-negative matrix factorization is introduced, the efficiency and the accuracy rate can be effectively improved.