The invention discloses a hyperspectral remote sensing data classification method based on deep learning, and belongs to the technical field of hyperspectral data classification. The invention aims to solve a problem of low classification precision of a method for classifying hyperspectral remote sensing data with nonlinear characteristics. The hyperspectral remote sensing data classification method comprises the following steps: firstly, processing hyperspectral original data to obtain the spectral feature vector and the spatial feature information of the hyperspectral original data; then, integrating the spectral feature vector with the spatial feature information; confirming labeled samples by hyperspectral integrated data, selecting a training sample and a test sample from the labeled samples; Pre-training a multi-layer restricted Boltzmann machine which forms a deep network by the training sample; carrying out supervised learning to the network formed by the multi-layer restricted Boltzmann machine through the training sample; and inputting the test sample into the trimmed network formed by the multi-layer restricted Boltzmann machine to realize hyperspectral remote sensing data classification. The invention is used for the hyperspectral remote sensing data classification.