The statistics of background information in hyperspectral target detection are often interfered by target information， and the presence of a large number of mixed pixels in hyperspectral images will further deepen this interference. In this study， we proposed a target detection algorithm using spectral unmixing to accurately calculate background information and significantly reduce the interference of target pixels on background statistical information. First， we obtained the abundance coefficient corresponding to the target end member by spectral unmixing and target similarity judgment. We combined it with the spectral angle coefficient to generate a reasonable background weighting coefficient for weighted constrained energy minimization （CEM） target detection， effectively improving the statistical accuracy of background information of mixed pixels. Second， we generated a preliminary result of target detection by utilizing the abundance coefficient corresponding to the target end member and spectral angle coefficient and fused with the weighted CEM target detection result to optimize further， effectively improving the robustness of the algorithm and target detection accuracy. Experimental results showed that the algorithm proposed in this study has good target detection performance for simulated or real hyperspectral images. The algorithm has strong robustness and effectively improves target detection accuracy. Compared with the traditional CEM algorithm， weighted CEM algorithm based on spectral angle， and normalized abundance coefficient as the target result the AUC of this study was promoted by an average of 0.071 2， 0.031 2， and 0.015 0， respectively. The proposed algorithm has strong practicability in hyperspectral applications.