In order to improve the recognition rate of the detection drone, the flight sound of the drone is analyzed in the time and frequency domain. For the sound characteristics of the drone, so as to better characterize the UAV sound signal, the Mel cepstrum coefficient (MFCC) and the inverted Mel cepstrum coefficient (IMFCC) are combined. The new feature parameters are used for feature dimensionality reduction based on Fisher criterion, construct the UAV “voiceprint library”, optimize the parameters in the support vector machine (SVM) through the gray wolf optimization algorithm (GWO), and establish the UAV audio Classification model. Experimental results show that the new feature parameters can make up for the low resolution of a single feature in the entire sound and audio domain. The GWO-SVM audio classification model can achieve drone detection within a distance of 50m, which has significant advantages over traditional detection methods.