Aiming at the problem of target detection in hyperspectral remote sensing images, a machine learning based method for target detection in hyperspectral remote sensing images is proposed. Firstly, the dynamic evolution algorithm is used to find the projection direction that maximizes skewness and kurtosis, and high-dimensional image data is projected onto a low dimensional subspace to extract spectral information from the image. Then, the extracted information is transformed into histogram form through linear discriminant analysis, and the target area is initially segmented and classified using automatic labeling watershed algorithm and KNN method to remove non target spectral pixels. The test results show that after detecting and processing the target information, the overall classification accuracy and average classification accuracy of image pixels have a numerical interval of [0.97, 0.98], and the information entropy value is only 0.01, indicating that the method has high accuracy and high credibility of the results.