The invention discloses a cell classifying method based on EMD feature extraction and sparse representation, and relates to an EMD-based cell feature extraction method. The method comprises steps: an orthogonal subspace projection (OSP) method is firstly used for carrying out band section on a medical hyperspectral image, dimensions are reduced, and data redundancy is reduced; and then, a two-dimensional EMD method is used for carrying out feature extraction on the data after dimension reduction, and the data are decomposed into a series of IMF components whose frequencies are arranged from high to low. A sparse representation classifier (SRC) is used for classifying the data, samples are classified through comparing residual, and when the residual obtained through calculation is small, the sample is divided into the class. In the cell classifying method based on EMD feature extraction and sparse representation, the EMD shows good time frequency characteristics, and an obvious potential and advantages exist in hyperspectral data feature extraction. Meanwhile, as the sparse representation classifier (SRC) is used, the classifying precision is more greatly ensured.