In the process of hard turning, the workpiece surface is easy to cause white layer, which affects the surface quality of the workpiece. The current methods of detecting white layer not only affects the processing efficiency, but also fail meet the requirements on full detection of product parts. The work aims to propose a prediction method of white layer on workpiece surface in hard turning based on gradient lifting decision to realize the real-time on-line detection of white layer phenomenon for each product in hard turning process, and improve the production efficiency and processing quality of products. The method mainly includes signal data acquisition, feature extraction and analysis, prediction model construction and prediction result analysis. Firstly, the normal (without white layer) and abnormal (with white layer) signal data in the process of hard turning were collected by power sensor, acoustic emission sensor and vibration sensor. Secondly, the time-domain parameters and wavelet packet energy parameters were extracted as the characteristic parameters to identify the white layer, and the influence degree of the above characteristic parameters was sorted by the feature importance analysis method, then the principal feature quantity related to the white layer was extracted by the PCA method as the input sample in the gradient boosting decision tree model. Thirdly, the grid search method was used to optimize the model parameters, reducing the dependence of the model on the amount of data and improving the accuracy of the model in predicting the white layer. Finally, based on the confusion matrix, a set of evaluation methods was proposed to ensure the prediction performance of the model. In order to verify the effectiveness of the above method, a hard turning experiment was carried out on the actual bearing products in the factory. The machining processing signal data was collected such as acoustic emission signal, three-way vibration signal and power signal for on-line prediction and identification of hard turning process; then the microstructure test specimens were prepared for the machined parts to explore the relationship between the white layer and the above sensor signals. The experimental results showed that compared with the characteristics of power and vibration signal, the characteristics of acoustic emission signal were more sensitive to the white layer. The model with acoustic emission signal characteristics greatly improved the accuracy, precision, recall, F1 value and Auc value. At the same time, compared with other model algorithms (SVM algorithm and XGBoost algorithm), the prediction accuracy based on the gradient boosting decision tree model can reach 90%, F1 score can reach 92% and Auc value can reach 89%, which can better reflect the white layer phenomenon. Based on various sensor technologies, combined with gradient boosting decision tree, this method can effectively predict and identify the white layer phenomenon in hard turning. This can not only realize the on-line intelligent prediction of the white layer phenomenon on the workpiece surface in hard turning, but also is of great significance to ensure the processing quality and improve the processing efficiency of products.