The invention relates to a hyperspectral data classification method based on a multi-layer convolution network and data organization and folding. The method comprises: step one, pretreatment is carried out before expanding and classification of three-dimensional hyperspectral data and a data matrix including effective spectrum information and a tag vector are obtained; step two, feature dimension expanding is carried out on the data matrix, and column-based folding and reorganization are carried out on a feature dimension to obtain a reorganized three-dimensional hyperspectral data input matrix; step three, a multi-layer convolution network structure parameter and an initial value are set; and step four, a feature and an error are calculated layer by layer by using forward propagation and BP algorithms, a network weight and a bias value are updated, iteration is carried out continuously to obtain a network stablity parameter, and then a network model for classification and a parameter for classification are obtained. Compared with other methods, the provided method has advantages of clear principle, clear structure, short identification time, and high detection identification rate; and the method being an effective classification method for hyperspectral data is suitable for rapid target detection and classification identification application of hyperspectral images.