Abstract: As an important equipment in mine production，the operation efficiency and stability of mine hoist directly affect mine productivity and safety. In order to solve the existing problems in energy sav⁃ ing and safety of mine hoist，a new power consumption model is proposed by analyzing the working principle of the hoist and identifying the main links and influencing factors of power consumption to systematically evaluate and compare different energy-saving measures. Based on artificial intelligence and machine vision technology，a series of improvement measures are proposed to improve the efficiency of the hoist，reduce en⁃ ergy consumption，and ensure worker safety. In terms of research methods，the machine vision technology is used to carry out real-time detection of the running state of the skip hook of the hoist，including the use of YOLOv7 for target detection，Segment Anything for example segmentation，and OpenCV for graph process⁃ ing and Angle calculation. By these technologies，the status of the hook can be analyzed in real time，and abnormal conditions can be warned and stopped in time，thus effectively avoiding potential safety risks. The research results show that the system can significantly improve the safety and automation level of the hoist， can more accurately detect the hook status，and timely alarm in abnormal circumstances，reduce the possi⁃ bility of accidents，and provide a certain reference for related research and mines.

Key words: machine vision, big data algorithms, artificial intelligence, instance segmentation, neu? ral network