Abstract: Drilling condition identification is a crucial measure to ensure operational safety and improve drilling efficiency. Currently, drilling conditions are commonly determined based on manual empirical formulas and threshold methods, leading to issues such as large data volume, low identification accuracy and slow decision-making speed. In order to enhance the efficiency of drilling condition identification, two important features, namely well depth variation and drill bit variation, were established by integrating domain knowledge. In view of the instability of on-site data transmission, a mobile window method was employed to select the most suitable window, thereby improving data stability. Additionally, an intelligent drilling condition identification model was constructed based on multiple algorithms, which uses evaluation indicators for analysis and model selection. The research results show that the model based on LightGBM performs exceptionally well in drilling operation condition identification, achieving a high accuracy of 98.9% with a processing time as short as 4.6 seconds. This affirms the efficiency and reliability of the proposed method, providing crucial theoretical and technical support for the efficient identification of drilling conditions.