The invention discloses a deep-learning-technology-based automatic accent classification method and apparatus. The method comprises: mute voice elimination is carried out on all accent voices in a training set and mel-frequency cepstrum coefficient (MFCC) feature extraction is carried out; according to the extracted MFCC feature, deep neural networks of various accent voices are trained to describe acoustic characteristics of various accent voices, wherein the deep neural networks are forward artificial neural networks at least including two hidden layers; probability scores of all voice frames of a to-be-identified voice at all accent classifications in the deep neural networks are calculated and an accent classification tag with the largest probability score is set as a voice identification tag of the voice frame; and the voice classification of each voice frame in the to-be-identified voice is used for carrying out majority voting to obtain a voice classification corresponding to the to-be-identified voice. According to the invention, context information can be utilized effectively and thus a classification effect better than a traditional superficial layer model can be provided.