Target detection from hyperspectral images is an important problem. Many target detection algorithms have been proposed and have been widely used in real applications during the past decades. However, the performance of these algorithms is highly susceptible to the quality of the target spectrum. This paper proposes a multi-priori learning algorithm to learning the inherent spectral similarity and difference between multiple priori target spectra, which can alleviate the target spectral variation by boosting the priori target spectra. Experiments on two hyperspectral images illustrated the effectiveness of the proposed algorithm.