Classification method based on sparse representation has won wide attention because of its simplicity and effectiveness, while how to adaptively build the relationship between dictionary atoms and class labels is still an important open question, at the same time most of the sparse representation classification methods need to solve a norm constraint optimization problem, which increases the computational complexity in the classification task. To address this issue, this paper proposes a novel Fisher constraint dictionary pair learning method to jointly learn a structured synthesis dictionary and a structured analysis dictionary, then directly obtains the sparse coefficient matrix by analysis dictionary. In this paper, the Fisher criterion is used to encode the coefficients. Finally the new method is applied to image classification task, the experimental results show that the new method not only improves the accuracy of classification but also greatly reduces the computational complexity. Compared with the existing methods, the new method has better performance.