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In order to improve the prediction efficiency and accuracy of the online tool wear monitoring system in the metal micro-milling process, a method based on linear discriminant analysis and improved BP neural network model to identify tool wear is proposed. This method collects the vibration signal of the micro-milling process through a sensor and a data acquisition system, extracts its time-domain and frequency-domain features, and performs dimensionality reduction through a linear discrimination method. The dimensionality-reduced features are input into the BP neural network model optimized and improved by the gray wolf, so as to realize the classification of the characteristics of the wear state of the micro-milling cutter. The results show that the proposed online monitoring method for micro-milling cutters can accurately identify various wear states of micro-milling cutters. In addition, compared with other classification algorithms, the proposed BP neural network model based on gray wolf optimization algorithm has comprehensive advantages in classification accuracy and computational efficiency. This has very important practical significance for monitoring the wear status of micro-milling cutters in the actual production process.