## 1 Introduction  
The frequent occurrence of rockburst disasters has been acknowledged as one of the most serious problems in underground projects all over the world because it directly threatened the safety of underground constructors, equipment, and buildings and even induced mine earthquakes [1]. In recent decades, many reliable measures have been studied to prevent rockburst. For example, [PERSON] et al. [2] applied borehole pressure relief technology to prevent rockburst in deep-buried mine roadways and achieved good results. [PERSON] et al. [3, 4] studied a new type of bolt support system, which had good energy absorption and rockburst prevention ability under the condition of tremor. However, in addition to finding reliable prevention and control measures, it is also important to establish an accurate rockburst prediction model in order to resist the occurrence of rockburst disasters [5, 6, 7, 8]. Presently, many scholars have studied the mechanism of rockburst from different perspectives and put forward corresponding prediction methods of rockburst intensity, for example, based on single factor strength theory, rigidity theory, energy theory, catastrophe theory, bifurcation theory, instability theory, [PERSON] criterion [7], [PERSON] criterion [8] and [PERSON] criterion [9], and random forest classification [10], cloud model theory [11], attribute comprehensive evaluation method [12], artificial neural network [13], matter-element extension theory [14], etc. All of the above methods have predicted rockburst from different perspectives and achieved certain prediction results. However,because rockburst is a very complex nonlinear dynamic phenomenon, it needs many methods to combine and complement each other to accurately predict the intensity of rockburst. Therefore, it is still necessary to introduce new theories and methods to study the occurrence of rockburst and intensity classification prediction. Based on fuzzy mathematics, [PERSON] [15] has proposed the variable fuzzy sets (VFS) method with relative membership degree (RMD) and synthetic relative membership degree (SRMD) at the core. This method establishes the corresponding VFS model according to the index classification level; then, the RMD function is established by using the VFS model corresponding to each level, and finally the method uses the SRMD function to combine the RMD values and weights of each index to get the final result. Compared with the fuzzy mathematics method, this method considers the dynamic variability and fuzziness of objective things, which improves the reliability of the prediction results [16]. Thus, the VFS method has been widely used in many multi-attribute decision-making problems, such as flood disaster risk assessment [16], surrounding rock stability assessment [17], water resources carrying capacity assessment [18], runoff prediction [19], and so on. Although the indexes and classification criteria are different in different evaluation types, the principle of the VFS method and the characteristics of RMD and SRMD functions are unchanged. Recently, the research of VFS mainly focuses on the application of the VFS method in different fields and the development of some new methods. For example, [PERSON] et al. [20] applied the VFS model to landslide stability evaluation; [PERSON] et al. [21] established a multi-index identification method for flood season stages based on VFS. On the other hand, some researchers also combined VFS with other methods to establish many new evaluation models, such as [PERSON] et al. [22], who combined VFS with fuzzy rough set and applied it to groundwater pollution assessment, and [PERSON] et al. [23], who used evidence theory and VFS to assess flood risk in Chengdu. Fewer studies, however, discuss two core issues of VFS method: (1) the characteristic relationship of RMDs at different classification levels and (2) the influence of index weight on the results of SRMD function calculation.