Hyperspectral anomaly detection is an unsupervised object detection algorithm designed to distinguish pixels that are significantly different from surrounding pixels.As an important means to achieve hyperspectral anomaly detection，local contrast usually adopts a fixed dual-window structure，which has average generalization ability and is easily affected by noise.To address these problems，a hyperspectral anomaly detection based on Taylor decomposition and adaptive window contrast is proposed.First，Taylor decomposition is used to decompose the spectral curve to further increase the difference between the background and the target.An adaptive window is used to ensure that the target is surrounded by the inner frame as much as possible to reduce the false alarm rate.The spectral angular distance is then used within the window to describe the differences in spectral curves.Finally，the contrast method is used to obtain the final anomaly detection results. Compared with seven advanced methods on four data sets，the results show that the proposed method has high detection accuracy and low false alarm rate.