The invention provides a Laplace spare deep belief network image classification method, and belongs to the field of image processing and deep learning. The method comprises the following steps that: firstly, on the basis of inspiration for primate visual cortex analysis, importing a punishment regular term into an unsupervised stage likelihood function, through a Lapalce sparse constraint, obtaining the sparse distribution of a training set while a CD (Contrastive Divergence) algorithm is used for maximizing a target function, and therefore, enabling unlabeled data to learn visual characteristic representation; secondly, putting forward an improved spare deep belief network, using Laplace distribution to induce the spare state of a hidden layer node, and meanwhile, using the scale parameter in the distribution to control spare strength; and finally, using a stochastic gradient descent method to carry out training learning on the parameters of the LSDBN (Laplace Spare Deep Belief Network). By use of the method which is put forwarded by the invention, even if the amount of each category of samples is small, best identification accuracy can be achieved all the time, and in addition, the method exhibits good spare performance.