Tool wear prediction plays an important role in ensuring the machining quality and efficiency， and reducing the machining cost， especially in the aerospace manufacturing field where difficult machining materials are widely used. Data and mechanism fusion model can combine the advantages of mechanism model and data-driven model. It is an effective way to realize tool wear prediction. However， the existing fusion methods are difficult to effectively balance the weight of data and mechanism on the model， which makes it difficult to really achieve the expected effect of the fusion model. To solve the above problems， this paper proposes a tool wear prediction method based on meta learning（ML） and PINN （Physics-informed neural network）. The solution space of the data-driven model is constrained by the wear mechanism， and the loss function of the fusion model is optimized by the meta learning algorithm to make rational use of the information provided by the data and mechanism. The experimental results show that the proposed method can effectively improve the accuracy and stability of tool wear prediction under varying cutting conditions.