es and advantages/disadvantages of different models in terms of early warning capabilities. It is found that in scenarios where the demand for characteristic parameters is low and the data sources are stable，machine learning algorithm models exhibit superior accuracy and timeliness in the early warning of drilling complex accidents. However，when faced with challenges such as high computational complexity，poor data quality，and unstable data sources，especially in predicting lost circulation during drilling and early-warning complex well kick accidents，the models suffer from high rates of false alarms and frequent missed alarms，and few field application cases are reported. To promote the field application of drilling complex accident early warning techniques and drilling digitalization，it is necessary to strengthen research on data quality improvement and algorithm optimization. Despite the numerous challenges，big data and artificial intelligence still open up broad prospects for early warning technologies.

The evaluation of rock drillability is of great significance in geological prospecting and drilling engineering. The traditional evaluation methods are mainly based on the core drillability testing，but due to the technical difficulties and high costs of coring，new unsupervised learning methods have become increasingly important. This study proposes a continuous formation drillability evaluation method based on well logging big data and unsupervised clustering algorithm to address this issue. Firstly，a self-organizing mapping neural network is used to cluster a large amount of well logging data and effectively extracting and classifying stratigraphic features. Then，by analyzing the penetration rate distribution of the formation corresponding to each cluster，the formation is graded by six drillability levels，thus achieving effective evaluation of the formation drillability. The core value of this study lies in utilizing big data and advanced unsupervised learning algorithms to overcome the reliance on a large number of core drillability test results in traditional methods，and deliver significantly improved evaluation performance. Through this method，the drillability classification of formations of the test well is successfully carried out，which validates the effectiveness of the method. The research results show that as the drillability level increases，the average penetration rate of the formation gradually decreases，and compared with the core test method，no notable deviation of the rock drillability level classification results is observed. This finding further confirms the importance and accuracy of this method in continuous formation drillability evaluation.