In the process of metal cutting, realizing effective monitoring of tool wear is of great significance to ensure the quality of parts machining. To address the tool wear monitoring (TWM) problem, a tool wear monitoring method based on data-driven and physical output is proposed. The method divides two Physical models (PM) into multiple stages according to the tool wear in real machining scenarios, making the coefficients of PM variable. Meanwhile, by analyzing the monitoring capabilities of different PMs at each stage and fusing them, the PM's ability to deal with complex nonlinear relationships, which is difficult to handle, is improved, and the flexibility of the model is improved; The pre-processed signal data features were extracted, and the original features were fused and downscaled using Stacked Sparse Auto-Encoder (SSAE) networker to build a data-driven model (DDM). At the same time, the DDM is used as a guidance layer to guide the fused PM for the prediction of wear amount at each stage of the tool, which enhances the interpretability of the monitoring model. The experimental results show that the proposed method can realize the accurate monitoring of tool wear, which has a certain reference value for the flexible tool change in the actual metal-cutting process.