To achieve full utilization of the spatial and spectral information in hyperspectral images and address the problems of insufficient training samples and small pixel targets being misidentified as background, we propose a hyperspectral target detection method based on spatial-spectral restructuring and operator weighting. This method eliminates the need for a coarse separation of target and background pixels and comprehensively learns image features through spatial-spectral combination learning. A feature enhancement coefficient was first derived using the hyperbolic tangent function to improve the contrast between the target and background. Principal component analysis was next employed to obtain a feature vector matrix that retains significant features, which was then used to construct an operator for projecting the data into a new principal component space. An operator was then used in this new space to weight the averages of the original image and the enhanced images, thus facilitating object detection. We tested this method on six hyperspectral image datasets, and the results show that the proposed method effectively detects targets and outperforms the comparison methods, achieving an average detection accuracy of 99.8% and thus verifying the accuracy and robustness of the proposed approach.