在信息论中，熵是对不确定性的一种度量。信息量越大，不确定性就越小，熵也

In information theory, entropy is a measure of uncertainty. The greater the amount of information, the smaller the uncertainty and the smaller the entropy; the smaller the amount of information, the greater the uncertainty and the greater the entropy. According to the characteristics of entropy, we can judge the randomness and disorder degree of an event by calculating the entropy value, and we can also use the entropy value to judge the dispersion degree of an indicator. The greater the impact. Therefore, the information entropy can be used to calculate the weight of each indicator according to the degree of variation of each indicator, which provides a basis for comprehensive evaluation of multiple indicators.

In information theory, entropy is a measure of uncertainty. The greater the amount of information, the smaller the uncertainty and the smaller the entropy, and the smaller the amount of information, the greater the uncertainty, and the greater the entropy. According to the characteristics of entropy, we can judge the randomness and disorder degree of an event by calculating entropy, and we can also use entropy to determine the degree of dispersion of an indicator, the greater the degree of discreteness of the indicator, the greater the impact of the indicator on the comprehensive evaluation. Therefore, according to the degree of variation of each indicator, the weight of each indicator can be calculated by using the information entropy tool, which provides the basis for the comprehensive evaluation of multi-indicators.

In information theory, entropy is a measure of uncertainty. The larger the amount of information, the smaller the uncertainty and the smaller the entropy; the smaller the amount of information, the greater the uncertainty and the greater the entropy. According to the characteristics of entropy, we can judge the randomness and disorder degree of an event by calculating entropy value, or we can judge the dispersion degree of an index by entropy value. The greater the dispersion degree of an index, the greater the impact of the index on comprehensive evaluation. Therefore, according to the variation degree of each index, the weight of each index can be calculated by using the tool of information entropy, which provides the basis for the comprehensive evaluation of multiple indexes.