Abstract: Adaptive cutting technology is crucial for enabling intelligent shearers, significantly improving mining efficiency, safety, and resource utilization. Therefore, a comprehensive review of adaptive cutting technology has been conducted, focusing on its technical principles and current applications. Based on core functions and technical objectives, adaptive cutting technology is categorized into four primary research areas: memory cutting, transparent geology, coal-rock identification, and adaptive control. Memory cutting enhance cutting paths by recording historical data, while transparent geology leverages integrated detection technologies to acquire real-time geological information. Coal-rock identification techniques are classified according to recognition principles: indirect methods based on physical parameters, direct methods relying on visual information, and wave-based detection methods such as ground-penetrating radar and ultrasound. Adaptive control automates the adjustment of shearer operating parameters. Collectively, these technologies advance the intelligence of coal mining machines from various perspectives. Nevertheless, due to geological complexities and challenging mining environments, existing technologies face limitations in adaptability and cost-effectiveness. Therefore, future development of adaptive cutting technology should focus on integrating memory cutting, transparent geology, and coal-rock identification technologies to enhance coal seam data acquisition. Implementing multi-sensor fusion technology to improve the accuracy and reliability of coal-rock identification. Developing intelligent decision-support systems based on big data analytics to optimize mining operations and researching multi-domain collaborative simulation control strategies to address technical challenges and improve system performance.