Abstract: As underground engineering projects such as mining, transportation, and hydropower continuously advance deeper into the earth, the frequency and intensity of rockburst disasters encountered during deep construction have significantly increased. Accurate prediction of rockburst intensity is becoming increasingly crucial for the prevention and control of rockburst disasters. This study reviews 23 domestic and international classification methods for rockburst intensity, and systematically compares the characteristics of 8 qualitative and 7 quantitative indicators used in these methods. Qualitative indicators often focus on the indentification of macroscopic features, such as acoustic features, kinetic behavior, temporal characteristics, and engineering impacts, which are susceptible to subjective experience. In contrast, quantitative indicators, such as strength-stress ratio and rockburst pit depth, seek to establish mapping relationships between measured data and rockburst intensity, though their scientific rigor requires further investigation. This study also introduces recent advances in coupled strength-stress criteria and computational methods for rockburst pit depth, which contributes more scientifically grounded quantitative indicators for rockburst intensity classification. Based on the review, the study envisions future trends in classification methods for rockburst intensity, including AI-driven intelligent and unmanned measurement technologies for quantitative indicators of rockburst, as well as automated quantitative analysis technologies for qualitative indicators of rockburst. These methods aim to reduce errors and safety issues arising from manual data collection. Additionally, the study suggests constructing a scientific and comprehensive rockburst database based on collected data and developing a multi-indicator rockburst intensity integrated classification model. Furthermore, it proposes to create an intelligent classification platform based on this model to achieve full-process automation in rockburst hazard assessment. Finally, given the frequent occurrence of rockburst disasters at the arch floor and tunnel face in deep construction, the study calls for exploring ways to classify rockburst intensity at these locations and for estimating rockburst pit depth.