The invention discloses an image data multi-label classification method. The method comprises the following steps: decomposing an input image, extracting high-order correlation of features by utilizing a neural network, decomposing tag data, extracting high-order correlation of tags by utilizing the neural network, and decoding a feature code of the input image from an input space to a tag space by adopting the neural network comprising multiple layers of full connection layers; constructing a loss function, initializing a training parameter, adopting a random gradient descent method to minimize a final loss function as a target, and training and solving to obtain an optimal training parameter; and inputting to-be-tested image data into the trained model for prediction, and outputting to obtain a label result to realize multi-label classification. According to the method, the problem that the secondary correlation and the multi-correlation of the labels cannot be extracted at the sametime when a person works in front of the image data is solved, the prediction difficulty caused by too sparse image data is reduced, and the accuracy of multi-label classification is improved.