Abstract: This paper proposes an extrinsic Fabry-Perot interferometer (EFPI) acoustic sensor based on a graphene membrane, combined with a cascade model of Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), for the detection and identification of various types of small rotorcraft drones. Furthermore, a detailed analysis is conducted on the feature extraction, data acquisition device, and the number of sound source categories involved in the experimental process.Through experimental comparison, it is found that when the Mel Frequency Cepstral Coefficient (MFCC) is used for feature extraction, the recognition effect is significantly better than that of the Short-Time Fourier Transform (STFT) and Mel Spectrogram features. Moreover, the accuracy rate of using this sensor for small rotary-wing drones detection and recognition is approximately 2% higher than that of an electrical microphone. In addition, this paper identifies the sound sources of various types of small rotary-wing drones, and the recognition accuracy is more than 95.33%, which proves that it can effectively detect and identify multiple types of drones.