The hyperspectral anomaly target detection algorithm for low rank sparse matrix decomposition ignores the problem that the spatial information of the image leads to low detection accuracy. In this paper, proposed is an improved low-rank sparse matrix decomposition method for hyperspectral anomaly target detection based on spatial information. The algorithm comprehensively utilizes the spectral signal and spatial signal of hyperspectral image, and combines with the sparsity of the image itself to improve the classical target detection algorithm based on low rank sparse matrix decomposition. The algorithm is built around the pixel to be measured. A spatial window of a certain size calculates both the spatial and spectral similarity weight of the central pixel and other pixels in the domain, and obtains the weight of the central pixel by calculating the proportional weight of other pixels in the domain to the central pixel. The spectral values are constructed and the residual matrix is obtained. Finally, the hyperspectral anomaly target detection algorithm based on low rank sparse matrix decomposition is used to obtain the sparse matrix of the image, and the sparse matrix and residual matrix representing the abnormal target information are added and solved. The euclidean distance between the matrix row vectors obtains the degree of abnormality of the pixels, sets the threshold, and obtains the detection result. In order to verify the detection performance of the proposed algorithm, real hyperspectral data is used for simulation experiments, and compared with the existing algorithms, higher detection accuracy can be obtained.