The invention discloses an electroencephalogram signal feature extraction and classification method of combining a denoising auto encoder (DAE) and a convolutional neural network (CNN). The method includes the steps of: collecting electroencephalogram data through an electroencephalogram signal collection instrument; carrying out preprocessing of abnormal-sample removing, mean removing, signal filtering and the like on the collected data; using the auto encoder, into which a noise coefficient is added, and electroencephalogram signals for training; using hidden layers of the denoising auto encoder as feature data output; then converting obtained feature data into an image-like format; utilizing the convolutional neural network for classification; and finally, utilizing a test data set to carry out a performance test on the trained network. Compared with other traditional methods, the method of the invention can obtain a higher classification accuracy rate and higher robustness.