The invention discloses a hyperspectral data classification method based on neighborhood information deep learning. The method comprises the steps of randomly dividing hyperspectral image data into atraining set and a test set; taking class attribution of the training set samples in the n * n neighborhood of each pixel point and the score of a front one main component of all the samples in the n* n neighborhood as the spatial information of each sample, putting the spectral information and the spatial information of each sample of the training set into a convolution neural network for modeltraining, putting the spectrum information and the space information of each sample of the test set into the model for classification result prediction. According to the method, the class attributionof the training set of each pixel point in the n * n neighborhood in a picture and the main component distribution of all the samples in the neighborhood serve as spatial information, spatial featureextraction is carried out on the neighborhood image through a two-dimensional convolution neural network, and fusion with the spectral dimension information is then conducted. The classification precision can be remarkably improved. The method has a good application prospect in the field of hyperspectral data classification.