Abstract: In order to monitor the wear status of the drill bit in the process of ultrasonic vibration drilling, an acquisition system for the vibration signal and AE signal of the ultrasonic vibration drilling bit is constructed. By collecting the vibration signal and AE signal of the drill bit under different wear conditions, it is subjected to wavelet decomposition. Obtain the eigenvalues related to the wear of the drill bit, merge the two as the input of the neural network, and input it into the constructed 12-10-3 BP neural network to identify the wear status of the drill bit. The test results show that the built BP neural network has an effective recognition rate of 91.7% for the drill bit through the fusion signal of vibration and AE, which can effectively identify the wear state of the drill bit.