针对由数据量过大带来的函数型综合评价中计算复杂度增 大或评价效率降低等问题, 文章将符号分析技术应用于函数型综合评价, 提 出了一种基于区间函数欧式距离的区间函数型聚类方法作为区间函数型综合 评价基本模型.由于综合评价的多指标区间函数特点, 提出区间函数熵值法 构建综合指标区间函数.与函数型综合评价相比较, 区间函数型综合评 价方法中增加了数据区间化的步骤, 能够在避免信息丢失的情况下, 更好地 抓取数据变动趋势, 提高了综合评价的效率.最后将文章提出的区间函数型综 合评价方法用于对股票的市场表现进行评价分析, 结果表明该方法在处理针对高频数据 的综合评价问题上具有一定的优势, 可以基于该方法开展应用研究.

In order to solve the problem that the computation complexity is increased and the evaluation efficiency is reduced in functional comprehensive evaluation (FCE) caused by excessive data, this paper applies symbolic analysis technology to FCE, and proposes an interval functional clustering method based on interval functional Euclidean distance as a basic model of interval FCE. Due to the characteristics of multiple variables in comprehensive evaluation, the interval functional entropy method is proposed to construct the comprehensive index interval function. Compared with the FCE, the interval FCE adds the step of converting point value into interval data, which can better grasp the change trend of data without losing information, so as to improve the efficiency of comprehensive evaluation. Finally, the interval FCE method proposed in this paper is used to evaluate and analyze the market performance of stocks. The results show that this method has certain advantages in dealing with the comprehensive evaluation of high-frequency data, and it can be further applied in the application research.