Abstract: In traditional rockburst hazard assessment methods, there are problems of large computational complexity and low precision in dividing hazardous areas. In order to meet the development needs of intelligent and visual prevention and control of rockburst, an intelligent assessment method for rockburst hazard areas based on image recognition technology is proposed. Using a semi quantitative estimation method, the method quantitatively characterizes the main controlling factors of dynamic and static loads for 11 types of rockburst hazards. Based on OpenCV machine vision library and deep learning model, the method achieves image recognition for a single main control factor. By constructing a mapping matrix between the grayscale of the image and the stress concentration coefficient, linear and nonlinear superposition of a single influencing factor is achieved to obtain the stress concentration coefficient matrix of the assessment area. Using the min max standardization method to construct a 4-level discrimination standard of "no, weak, moderate, and strong" for the hazard area of rockburst, the method achieves graded and division assessment. A software for intelligent assessment of rockburst hazards is developed based on Python language, and the actual application effect of the software is tested. The results show that the software improves the traditional one-dimensional linear hazard area division method for roadways to a two-dimensional plane division method for the entire mining space. It significantly improvies the assessment efficiency and precision of hazard area division and reduces labor costs. The assessment results are highly consistent with the microseismic energy density cloud map and the on-site measured mining pressure pattern, which can provide effective guidance for the prevention and control of on-site rockburst.