Abstract: An analysis was conducted on the identification methods of underground drilling conditions in coal mines from three aspects: parameter collection, data processing, and abnormal condition recognition. A framework for identifying common underground drilling conditions in coal mines was proposed, consisting of a data collection layer, a processing layer, and a condition recognition layer. Among them, the data acquisition layer can collect drilling parameters; the data processing layer includes data cleaning of outlier points, extraction of feature parameters, and fusion of multi-source information from sensors; the working condition recognition layer adopts classification algorithms and optimization algorithms in machine learning, and combines two or more recognition algorithms to form a hybrid intelligent working condition recognition algorithm. It learns data and trains models for drilling parameters with working condition classification labels, ultimately achieves intelligent recognition of drilling working conditions. Based on typical drilling parameters such as torque, pump pressure, and drilling speed in a coal mine in Huainan, Anhui Province, a nuclear extreme learning machine (KELM) recognition model optimized using the whale algorithm (WOA) was constructed to identify typical working conditions. By learning from the training set samples, the WOA-KELM model with higher recognition accuracy than SVM, KNN, and other recognition models was adopted to achieve intelligent recognition of typical working conditions.