Aiming at the problem of low prediction accuracy of milling tool wear volume,a high-precision milling tool wear volume prediction method was proposed.The optimal parameters of the long short-term memory network (LSTM) was found through the genetic algorithm (GA),and the parameters were input into the LSTM to realize the improved model GA-LSTM.The time domain,frequency domain and frequency domain methods were used to extract features,and the Pearson correlation coefficient method was used to screen out the feature vectors that are highly similar to the milling tool wear volume,the GA-LSTM model was input for training,and the test data were predicted.The experimental results show that compared with the traditional machine learning methods BPNN or deep learning methods FE-LSTM,CNN,the root mean square error of GA-LSTM decreases by 41.3%,39.0% and 51.5%,and the mean absolute percentage error decreases by 48.3%,40.8% and 56.7%,respectively,the prediction and recognition accuracy of the model is greatly improved,and the effective prediction of the milling tool wear volume is realized.