Abstract: With the shift of coal mining from shallow to deep, the number of coal mines facing the risk of rockburst disasters continues to increase, and the situation of prevention and control of rockburst disasters becomes more severe.In the past three years, the national coal regulatory administration has issued a number of policy guidance documents for rockburst disasters.However, an effective remote monitoring mechanism for rockburst disasters has not been established at the regulatory level, and there is a lack of relevant systems for an integrated display and risk analysis in a visual way, which is unable to provide scientific support for remote monitoring, accurate law enforcement and scientific control.Based on the understanding on the mechanism of rockburst hazard and risk monitoring and research, this paper proposes to construct a national system for disaster risk intelligence analysis, using the new technologies such as artificial intelligence, big data to comprehensively analyze the monitoring results from each mine.The system can be used as remote supervision on national rockburst hazard, accurate law enforcement and scientific controls to provide reasonable solution.There are three key scientific and technical problems in the construction of intelligent monitoring and analysis system of coal mine rockburst disaster risk including ① the effectiveness of multi-source heterogeneous data access; ② the difference of disaster risk index of rockburst; and ③ the intelligent analysis of disaster risk classification.This paper has developed a three-layer technical architecture covering data layer, service layer and application layer to solve these three problems.In this architecture, a unified data standard, transmission mode and access process are proposed to address the validity of multi-source heterogeneous data access for rockburst disasters.In view of the difference of the rockburst disaster risk index, a thought of horizontal comprehensive analysis, which combines with all kinds of rockbursts nationally, is proposed.On the basis of a large number of samples, disaster risk indicators are gradually unified, and a scientific and comprehensive risk assessment system for rockburst disasters is established.This system has the comprehensive service capability of single-region multi-parameter analysis and multi-region deep learning.It is pointed out that in the face of real-time import of massive monitoring data in the future, it is necessary to introduce big data and artificial intelligence-related algorithms to conduct deep mining of effective information.At present, the coal mine perception data acquisition and analysis platform of the national coal regulatory administration can provide a good data processing basis for subsequent intelligent analysis.Based on this platform, the technical framework of the coal mine ground pressure disaster risk monitoring and intelligent analysis system is proposed.