Disclosed is a brain cognitive state judgment method based on a non-negative tensor projection operator decomposition algorithm. The method includes the steps of S1, collecting brain functional magnetic resonance images under different cognitive tasks to form a data sample set, carrying out preprocessing, and forming a sample set according to tensor modes, wherein the sample set is divided into a training set and a testing set according to the cognitive tasks, and the training set comprises functional magnetic resonance data in similar proportion of different cognitive states, S2, computing non-negative tensor projection operator decomposition of the training sample set to solve out a non-negative feature transformation matrix, projecting training samples to a non-negative tensor feature sub-space for dimensionality reduction to obtain a non-negative feature tensor set of the training set, S3, using lower-dimension non-negative feature tensor data after dimensionality reduction as input of an STM for training to solve out the optimum projection direction of the STM, and S4, projecting brain functional magnetic resonance data of tested samples to the non-negative tensor feature sub-space obtained through training to obtain non-negative feature tensors of the brain functional magnetic resonance data in the sub-space, and inputting the non-negative feature tensors of the tested samples to the trained STM to judge cognitive state types of the non-negative feature tensors.