Uncertainty measurements underlie the system interaction and data learning. Their relevant studies are extensive for the single-valued decision systems, but become relatively less for the interval-valued decision systems. Thus, three-level and three-way uncertainty measurements of the interval-valued decision systems are proposed, mainly by systematically constructing vertical-horizontal weighted entropies. Firstly, the interval-valued decision systems are endowed with three-level structures, including Micro-Bottom, Meso-Middle, and Macro-Top. Secondly, a three-level decomposition is hierarchically made for the existing conditional entropy. Thirdly, three-way weighted entropies are systematically and hierarchically constructed at the three levels, and they achieve their hierarchy, systematicness, algorithm, boundedness, and granulation monotonicity/non-monotonicity. The three-level and three-way weighted entropies deepen and extend the conditional entropy, and they realize the ingenious criss-cross informatization for the interval-valued decision systems. Their effectiveness of uncertainty measurements is ultimately verified by table examples and data experiments.

不确定性测量是系统交互和数据学习的基础。他们的相关研究对于单值决策系统而言是广泛的，但对于区间值决策系统则相对较少。因此，提出了区间值决策系统的三级和三向不确定性度量，主要是通过系统地构建垂直水平加权熵来实现的。首先，区间值决策系统具有三层结构，包括微底，中观和宏观顶。其次，对现有条件熵进行三级分解。第三，在三个层次上系统地和分层地构造三向加权熵，并实现它们的层次，系统性，算法，有界性，和颗粒单调/非单调。三级和三级加权熵加深并扩展了条件熵，它们实现了区间值决策系统的巧妙的十字交叉信息化。表格示例和数据实验最终验证了其不确定性测量的有效性。