A rockburst is a geological disaster that occurs in resource development or engineering construction. In order to reduce the harm caused by rockburst, this paper proposes a prediction study of rockburst propensity based on the intuitionistic fuzzy set-multisource combined weights-improved attribute measurement model. From the perspective of rock mechanics, the uniaxial compressive strength σc, tensile stress σt, shear stress σθ, compression/tension ratio σc/σt, shear/compression ratio σθ/σc, and elastic deformation coefficient Wet were selected as the indicators for predicting the propensity of rockburst, and the corresponding attribute classification set was established. Constructing a model framework based on an intuitionistic fuzzy set–improved attribute measurement includes transforming the vagueness of rockburst indicators with an intuitionistic fuzzy set and controlling the uncertainty in the results of the attribute measurements, as well as improving the accuracy of the model using the Euclidean distance method to improve the attribute identification method. To further transform the vagueness of rockburst indicators, the multisource system for combined weights of rockburst propensity indicators was constructed using the minimum entropy combined weighting method, the game theory combined weighting method, and the multiplicative synthetic normalization combined weighting method integrated with intuitionistic fuzzy sets, and the single-valued data of the indicators were changed into intervalized data on the basis of subjective weights based on the analytic hierarchy process and objective weights, further based on the coefficient of variation method. Choosing 30 groups of typical rockburst cases, the indicator weights and propensity prediction results were calculated and analyzed through this paper’s model. Firstly, comparing the prediction results of this paper’s model with the results of the other three single-combination weighting models for attribute measurement, the accuracy of the prediction results of this paper’s model is 86.7%, which is higher than that of the other model results that were the least in addition to the number of uncertain cases, indicating that the uncertainty of attribute measurement has been effectively dealt with; secondly, the rationality of the multiple sources system for combined weights is verified, and the vagueness of the indicators is controlled.

A rockburst is a dynamic disaster that may result in considerable damage to mines and pose a threat to personnel safety. Accurately predicting rockburst intensity is critical for ensuring mine safety and reducing economic losses. First, based on the primary parameters that impact rockburst occurrence, the uniaxial compressive strength (σc), shear–compression ratio (σθ/σc), compression–tension ratio (σc/σt), elastic deformation coefficient (Wet), and integrity coefficient of the rock (KV) were selected as the evaluation indicators. Second, an improved game theory weighting method was introduced to address the problem that the combination coefficients calculated using the traditional game theory weighting method may result in negative values. The combination of indicator weights obtained using the analytic hierarchy process, the entropy method, and the coefficient of variation method were also optimized using improved game theory. Third, to address the problem of subjectivity in the traditional unascertained measurement using the confidence identification criterion, the distance discrimination idea of the Minkowski distance was used to optimize the identification criteria of the attributes in an unascertained measurement and was applied to rockburst prediction, and the obtained results were compared with the original confidence identification criterion and the original distance discrimination. The results show that the improved game theory weighting method used in this model makes the weight distribution more reasonable and reliable, which can provide a feasible reference for the weight determination method of rockburst prediction. When the Minkowski distance formula was introduced into the unascertained measurement for distance discrimination, the same rockburst predictions were obtained when the distance parameter (p) was equal to 1, 2, 3, and 4. The improved model was used to predict and analyze 40 groups of rockburst data with an accuracy of 92.5% and could determine the rockburst intensity class intuitively, providing a new way to analyze the rockburst intensity class rationally and quickly.