ing to solve the problem that the complex background pixels affect the hyperspectral classification accuracy, the object detection theory is introduced into the hyperspectral image classification domain, and a hyperspectral image classification method based on spectral-spatial feature iteration is proposed. A multi-target constrained classifier (MTCC) is designed by constrained energy minimization method. Based on the detection theory, the MTCC can effectively decrease the influence of complex background data on the classification accuracy. At the same time, to eliminate the over-classification problem caused by the spectral features, the method uses the feedback fusion of spectral-spatial to strengthen the spatial enhancement information so as to improve the classification accuracy gradually. The results of the experiments on the data sets of Purdue, Salinas and Pavia show that the average accuracies of the proposed methods are 98.09%, 97.33% and 84.68% respectively, and the precisions of the proposed method are 96.84%, 95.32% and 79.13% respectively. Compared to other algorithms, the proposed method has higher generalization ability and practicability.