Hyperspectral imagery cube Data can provide spatial information and diagnostic spectral characteristics, in the range of visible and near-infrared wavelength, about the attributes of materials in the scene, which contribute to the unique advantage of hyperspectral imagery for target detection. However, hyperspectral target detection has some shortcomings, such as the classical hyperspectral target detection algorithm only uses the information of spectral dimension to detect the target. The detection model either has insufficient accuracy for the construction of background high-dimensional f characteristics matrix or has high requirements for the completeness of background prior spectral characteristics, resulting in poor adaptability of the algorithm to different scenarios. Therefore, based on the classic multi-target detection algorithm-multiple target constrained energy minimization (MCEM), which has low computational complexity, fewer parameter requirements and better detection performance, this paper proposes a modified algorithm R-MCEM (revised MCEM) based on the separation model of target and background. First of all, this algorithm designs a pixel-by-pixel moving operation window that is similar to the shape and size of the target and sequentially calculates the spectral distances between each pixel and other pixels in the window, referred to as D1, and the spectral distances between the pixel and all targets,referred as D2. Next, all pixel values in the window are replaced with the pixel obtaining the minimum value of D1/D2. Then, move the window from left to right and from top to bottom and repeat the same calculation. Until the moving operation window traverses the entire hyperspectral image, all the interested targets in the hyperspectral image are eliminated as much as possible, and the accuracy of the background high-dimensional characteristics matrix is greatly improved. In this paper, the performance verification tests of the modified detection algorithm based on the true hyperspectral image data and the simulated image data are designed respectively, and the detection accuracy evaluation of the proposed algorithm is carried out by the three-dimensional receiver operation characteristics curve (3D ROC) combined with the Separation Degree Between Background and Target（SDBT）. The experimental results show that the proposed algorithm can effectively reduce the false alarm rate and improve the detection accuracy. The detection accuracy and SDBT based on the actual data are increased from 0.937 7 and 0.61 of the MCEM algorithm to 0.993 5 and 0.71 of R-MCEM, and the sub-pixel detection ability based on the simulated data is increased from 20% abundance of MCEM to 15% abundance of R-MCEM.