The invention discloses a hyperspectral image compressive sensing method based on nonseparable sparse prior. The hyperspectral image compressive sensing method based on nonseparable sparse prior is used for solving the technical problem that existing hyperspectral image compressive sensing methods are low in reconstruction precision. According to the technical scheme, a few of linear observed values of each pixel spectrum are collected and serve as compressed data, and the resource demand in the image collection process is reduced while substantial data compression is achieved. In the reconstruction process, empirical Bayesian reasoning is utilized to construct nonseparable sparse prior of sparse signals, potential correlation among nonzero elements in the sparse signals is taken into full consideration, and high-precision reconstruction of hyperspectral images is achieved. Because a wavelet orthogonal basis serves as a dictionary according to the method, dependency on end members is eliminated. In addition, through reasoning based on a Bayesian framework, full-automatic estimation of all unknown parameters is achieved, human adjustment is not needed, and adaptability is wide. Experiments show that when the sampling rate is 0.1, the peak signal to noise ratio obtained according to the hyperspectral image compressive sensing method is increased by above 4 db compared with that obtained according to a background technology compressive sensing method.