With the continuous development of hyperspectral image technology, spectral resolution and spatial resolution are continuously improved, and finer spectral features can be obtained compared to other remote sensing images. This provides a theoretical platform for the research fields of high-precision classification, demixing and target detection of features, in which the hyperspectral anomaly target detection technology does not require a priori information of the features, which is more in line with the needs of practical applications, and has become a research hotspot. For most hyperspectral anomaly target detection algorithms, only focusing on the spectral difference between the target and the backgroundand neglecting the difference in spatial structure between the two to result in low detection accuracy, a space-spectrum joint anomaly degree for hyperspectral anomaly detectionalgorithm is proposed. The algorithm does not need to assume the background model of the image, based on the sliding double window, and proposes two concepts of the spectral anomaly and spatial anomaly. In the calculation of spectral anomaly, the nonlinear characteristics between the bands are considered, and the kernel function method based on spectral angle matching is used for detection. Based on the two-window model, the nuclear spectral angles of the central pixel and the local background pixel are calculated one by one and set. The threshold value is used to obtain the spectral anomaly of the central pixel; in the calculation of the spatial anomaly, due to the spatial clustering property of the matter, the image block gray vector representing the pixel class can be obtained by constructing the spatial window model of the pixel point. At the same time, the Euclidean distance of the image block gray vector between different pixels is solved and the threshold is set to obtain the spatial anomaly of the central pixel. Finally, the spectral anomaly of the central pixel and the spatial anomaly are summed to obtain the center. The spatial anomaly joint anomaly of the pixel, based on the sliding double window model, detects the pixels of the whole image one by one, and the abnormal detection result of the image can be obtained. The simulation results of the proposed algorithm are carried out by using three sets of real hyperspectral data of AVIRIS, and compared with the traditional RX algorithm, LRX algorithm and KRX algorithm. The results show that the proposed algorithm has better detection effect, compared with KRX algorithm. The running speed has a large increase.