The invention discloses a voiceprint identification method based on global change space and deep learning hybrid modeling, comprising the steps of: obtaining voice segment training data, employing a global change space modeling method to perform an identity authentication vector to obtain a TVM-IVECTOR; employing a deep neural network method to perform training to obtain an NN-IVECTOR; fusing two vectors of a same audio frequency file to obtain a new I-IVECTOR characteristic extractor; for the audio frequency to be tested, fusing the TVM-IVECTOR and the NN-IVECTOR, and then extracting a final I-IVECTOR; and after channel compensation, performing rating identification on the speaker model in a model base to obtain an identification result. The voiceprint identification method possesses greater robustness to environmental factor interference such as environment mismatching, multiple channel change and noise, and can improve voiceprint identification method performance.