To deal with the problems of scarce data of machine tool wear and low recognition accuracy of tool wear status, a tool status recognition method based on sample expansion and improved domain adversarial training of neural networks (SE-IDANN) was proposed. First, to solve the problem of scarce machine tool wear data, two feature extractions on the machine tool data were performed, and the sample was expanded through the Smooth algorithm. Secondly, a residual block was added to the domain adversarial training of neural networks (DANN) feature extractor to further extract effective feature information and solve the problem of weak tool wear characteristics. Finally, to realize the accurate identification of tool wear, the Wasserstein distance used as the data distribution similarity standard between the target domain and the source domain was introduced into the DANN model. Through the analysis and test verification of machine tool data, it is proved that this method can better identify tool wear.