Abstract: The limited variety and quantity of sensors deployed on drilling rigs make it difficult to identify the current execution process during the intelligent construction process of coal mine drilling rigs, therefore, an operation processes identification method is proposed, which includes hierarchical modeling of drilling rig operation and probability inference of current execution process. Firstly, the coupling process among the components of different granularity is described and modeled based on the hierarchical analysis method, which reveals the interactive characteristics between equipment, function and system during the execution of each process. Secondly, Bayesian network is introduced to establish a process execution probability inference model based on the above hierarchical model of the drilling operation process, which analyzes the causal relationship between different granularity components and the drilling processes. Then, the collected sensing data is processed and provided as a realtime evidence to the probability inference model, thereby obtaining the execution probability of each drilling process. Finally, the hydraulic pressure value, the rotational speed and the movement speed of the drilling head are provided as input to the probability inference model to obtain the execution probability of drilling process, and the accuracy of the result reaches over 81%. The experiment proves that the method proposed in this paper is practical and feasible. The above research provides a hierarchical decoupling method for the drilling processes and an analysis method for the interaction process between different granularity components of drilling rigs, providing technical support for the research on intelligent control methods of drilling rigs and the development of advanced intelligent geological equipment.