In recent years, block caving has drawn the attention of many mine enterprises due to the admired extraction rate and lower cost, which can exploit the materials via gravity inflow. At the same time, the limitation of this advanced method cannot be underestimated easily, such as surface subsidence and boulder, usually, the latter leads to the frequent secondary blast and damage of bottom structure. Thus, it is significant and crucial to evaluate the fragmentation before the implement of this method. But, traditional fragmentation assessment model suffers from the complex process of modeling and simulation. In this study, a hybrid model consists of unascertained measurement theory and information entropy was constructed to meet the requirements of this prospective mining method. Considering the influence of various parameters on rock fragmentation at the same time, twenty-three factors (i.e., uniaxial compressive strength, modulus ratio, fracture frequency, aperture, persistence, joint orientation, roughness, infilling, weathering, in situ stresses, stress orientation, stress ratio, underground water, fine ratio, hydraulic radius, undercut height, draw column height, draw points geometry, draw rate, multiple draw interaction, air gap height, broken ore density and undercut direction) were chosen to extract the main characteristics of rock mass samples from the two different mines, namely Reserve North (Chile), Diablo Regimiento (Chile) and Kemess mine (Canada). A new membership function (logarithm curve) was added to eliminate uncertainty results from the low level of knowledge about rock mass properties. Then, information entropy was performed to quantify the impacts of individual index. A credible degree identification criterion (R_η) was also applied to review the sample attributes qualitatively. Ultimately, degree of fragmentation of the three samples was judged easily on the basis of a composite measurement vectors and R_η. The evaluation results showed that the fragmentation grades of Reserve North, Diablo Regimiento and Kemess mine, separately, were “Good”, “Medium” and “Good”. With regard to the excellent performance of this hybrid model, it can be seen as a reliable approach to describe the fragmentation potential during the ore extraction using block caving mining method.

近年来，由于令人赞叹的开采速度和较低的成本，块状崩落吸引了许多矿山企业的注意，它们可以通过重力流入来开采材料。同时，这种先进方法的局限性不容易被低估，例如地面下沉和巨石，通常，后者导致频繁的二次爆破和底部结构的破坏。因此，在实施此方法之前评估碎片化意义重大且至关重要。但是，传统的碎片评估模型遭受了建模和仿真的复杂过程的困扰。在这项研究中，由不确定的测量理论组成的混合模型和信息熵被构造来满足这种预期采矿方法的要求。北部保护区（智利），暗黑破坏神雷基明托（智利）和凯姆斯矿山（加拿大）。添加了新的隶属度函数（对数曲线），以消除由于对岩体特性了解不足而导致的不确定性结果。然后，执行信息熵以量化各个索引的影响。甲可信度识别准则（ř _η）也适用于检查所述样品定性属性。最终，三个样品的断裂的程度的复合测量矢量和的基础上容易地判断ř _η。评价结果表明：Reserve North，Diablo Regimiento和Kemess分别是“好”，“中”和“好”。关于这种混合模型的出色性能，可以将其视为描述使用块崩探采矿法提取矿石过程中碎裂潜力的可靠方法。