Abstract: With increasing coal mining depth, the rockburst risk is becoming more serious.A thorough understanding of rockburst occurrence mechanism, monitoring and prevention technology is crucial to coal mine safety.In this paper, we systematically summarized the research findings of the team, put forward the mechanismthat the superposition of static and dynamic loads causing rockburst, and accordingly subdivided the rockburst types based on the stress and energy body.We proposed the three field coupling monitoring principle for rockburst risk, including stress field, seismic field and energy field.Based on the three stress fields, we further established a multi-parameter comprehensive monitoring and pre-warning system as well as a space-temporal pre-warning model. Based on big data and cloud platform technology, we developed a cloud platform for intelligent identification and multi-parameter monitoring and pre-warning of rockburst risk.The cloud platform realized the integration of rockburst monitoring data and prevention measures information, and further improved the monitoring and pre- warning efficiency of rockburst risk.Finally, we proposed two theories, i.e., the strength weakening theory for rockburst risk reduction and the strong-soft-strong structure theory, and put forward the monitoring and prevention scheme for different rockbursts.The study results strongly promote the research of rockburst and provide significant guidance for the safety of coal mines suffering rockburst