Abstract: At present, the walking and drilling operations of the mine kilometer directional drilling rig are all realized by the manual operation of drillers. The intelligence level is low. At present, there is no research on the correlation between the action type of kilometer directional drilling rig and the vibration state of the hydraulic pump station. Therefore, it is difficult to remotely identify the action type of the kilometer directional drilling rig. In order to solve the above problems, an action recognition method for mine kilometer directional drilling rig based on empirical wavelet transform (EWT) and fuzzy C-means (FCM) clustering algorithm is proposed. Firstly, the EWT method is used to analyze the frequency characteristic information of the three key parts (motor, hydraulic pump and coupling) of the hydraulic pump station when the kilometer directional drilling rig performs five different actions (the start of the kilometer directional drilling rig, the rotation of the power head without drill pipe, the rotation with drill pipe, the forward slow drilling with drill pipe and the forward fast drilling with drill pipe). The vibration signals in the most obvious direction of each vibration characteristic are selected to form the original signal group for action recognition. Secondly, the combination of EWT decomposition and correlation coefficient selection rules is used to extract eigenvectors containing drill action information in the original signal group for action recognition. The weight of different eigenvectors is confirmed. The standard recognition eigenvector is constructed. Finally, the membership degree between the action eigenvector to be identified and the five action recognition standard eigenvectors is obtained by using the FCM clustering algorithm. The intelligent recognition of the action types of the kilometer directional drilling rig is realized. Taking the ZYL-17000D type mine kilometer directional drilling rig as the research object, the reliability of the action recognition method based on EWT and FCM clustering algorithm for mine kilometer directional drilling rig is verified by experiments. The vibration data of the motor, hydraulic pump and coupling in the axial, horizontal and vertical radial directions under five actions are collected in the experiment. The results show that the empirical wavelet functions of the vibration signals of the motor, hydraulic pump and coupling of the drilling rig show different characteristics when it performs different actions. The clustering performance of the eigenvectors of the axial vibration signals of hydraulic pumps is the best. According to the difference of extracted eigenvectors under different actions, action types can be identified. The results of action recognition based on test data show that this method can effectively identify the action type of kilometer directional drilling rig, and the recognition accuracy is 96.8% when the membership degree is greater than 0.9.