Aiming at the problem that the single parameter representation in the transformer fault diagnosis method is not comprehensive enough, a dry-type transformer fault diagnosis model based on the mixed characteristics of MGCC is proposed. First, the preprocessed dry-type transformer noise signal passes through the Mel filter and the Gammatone filter to obtain the MFCC with general anti-noise performance and the more robust GFCC characteristic parameters; then, the two parameters are linearly superimposed and the Fisher is used to compare with discarding the components with lower contribution rate, the mixed parameter MGCC is obtained; finally, it is sent to the LSTM classification model for pattern recognition. Calculation results show that the fault diagnosis rate of the proposed mixed feature MGCC is as high as 96.11%, which has better accuracy and noise immunity than a single cepstrum feature parameter of the acoustic signal.