The invention provides a non-negative matrix factorization clustering method based on dual local learning. The non-negative matrix factorization clustering method based on dual local learning comprises the steps of S10, selecting a to-be-classified data matrix V, a cluster number a1 and a cluster number a2 according to a to-be-clustered image; S20, constructing an objective function O according tothe data matrix V; S30, outputting a class result by using an iteration method according to the objective function O; and S40, clustering the to-be-clustered images according to the class result. According to the clustering method, double-structure learning is combined, a collaborative clustering problem is converted into a non-negative matrix v problem with orthogonal constraint, the complexityof the problem is simplified, the method has representativeness and universality, the complexity is low, the operation speed in the clustering process is greatly increased, and the clustering efficiency is improved.