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.