To fully utilize the spatial and spectral information of hyperspectral images and suppress image noise, a hyperspectral anomaly detection method based on local contrast and multidirectional gradient analysis is proposed. First, to leverage local spectral information, a local contrast strategy is introduced, generating a spectral detection score map based on the brightness difference between the target and the background. Then, to reduce computational complexity, a spectral fusion-based dimensionality reduction technique is proposed to process hyperspectral images. In addition, a local multidirectional gradient feature method is proposed to reduce image noise, retain local detail features, and generate a multidirectional gradient detection score map. Finally, the anomaly result map is obtained by fusing the spectral and gradient-based score graphs. Experimental results on four classical datasets demonstrate that the proposed method can successfully display abnormal targets in the result graph, achieving higher detection accuracy and lower false alarm rates compared to seven existing methods.