Abstract: As the core equipment for hoisting and transporting ores, personnel, and equipment, the deep well hoisting system plays a crucial role in mine production. Research on intelligent health monitoring technology is of great significance for ensuring the safe and reliable operation of the hoisting system and the efficiency of mine production. This paper first introduces the structural composition and functions of the deep well hoisting system, and analyzes the operating environment of key components and their health status monitoring requirements. Then, it elaborates on fixed-point perception technology based on multi-source signals such as vibration, sound, vision, and temperature, as well as mobile perception technology based on mobile robot inspection and container-integrated inspection. Next, it presents the application of intelligent health status assessment methods based on signal processing, machine learning, and deep learning in the condition monitoring and assessment of key components of various hoisting systems. Furthermore, it summarizes the development status and technical characteristics of traditional monitoring platforms and digital twin platforms. Finally, it points out the existing problems and challenges of current health monitoring technology for deep well hoisting systems, and proposes potential future research directions with research value from aspects such as complex working condition perception optimization, mobile perception intelligence, assessment model efficiency, and monitoring system integration.