Aiming at the problem of low accuracy when predicting milling tool wear,a milling tool wear prediction method was proposed based on a long short-term memory neural network (LSTM) optimized by the black widow algorithm (BWO) combined with the AdaBoost integrated learning algorithm.The time domain,frequency domain and time-frequency domain multi-domain features were extracted from the milling tool wear vibration signal.The core parameters of the LSTM were optimized by the BWO algorithm,and the optimized LSTM network was combined with the AdaBoost algorithm to build a milling tool wear prediction model.Finally,experiments were conducted with the whole life cycle vibration data of the milling tool of PHM Society 2010.The results show that the proposed method can effectively predict the variation of milling tool wear,and the mean absolute percentage error of the optimized model is 3.436%,root mean squared error is 6.471 and R2 is 0.935 when compared with the pre-optimized model.This method can obtain the prediction value of milling cutter wear with higher accuracy and higher prediction efficiency.