Due to excellent physical and mechanical properties， carbon fiber reinforced plastics（CFRP） and titanium alloys （TC4） were often widely used in the aerospace industry as laminated structures. Since CFRP and TC4 were both typical difficult-to-machine materials， and had different mechanical and thermal properties， the tool wear was rapid during the hole-making process， which affected the machining quality. In order to ensure the quality of drilling and timely replacement of cutting tools， a tool wear condition monitoring model based on convolution neural network-long short time memory（CNN-LSTM） was established. The model took the feature of force and acoustic emission signals with strong correlation to tool wear as input and the tool wear condition labels as output to realize tool wear monitoring. The results show that the model has an accuracy rate of 97.222%， which can effectively monitor the tool wear status during the drilling process of CFRP/TC4 laminated structures.