[目的/意义] 回顾现有的睡美人文献识别方法，梳理不同方法的优缺点，尝试兼顾准确性与易操作性来改进睡美人文献的识别方法。[方法/过程] 基于目前发展较为成熟的Bcp指数识别法，借鉴其利用引文曲线"离散程度"进行识别这一核心思想，引入统计学中的"变异系数"概念，将其应用于不同引文曲线类型的区分，从而提出用以识别睡美人文献的PCV指数。[结果/结论] 识别结果显示，PCV指数能够较为简单、准确地识别睡美人文献，且该方法对总被引次数具有较低的依赖性。
[Purpose/significance] This paper aims to review existing identification methods of sleeping beauties in science, discuss strengths and weaknesses of different kinds of methods, and put forward a brand-new method for identifying sleeping papers. [Method/process] This study is based on the Bcp index, which is a well-developed and accurate method for identifying sleeping beauties in science. Through referring to the core idea of using the "dispersion degree" of citation curve for identification, the concept of "coefficient of variation" in statistics is introduced to the new method. Then the PCV index is proposed to identify various citation curves, sleeping beauties in particular. [Result/conclusion] As is shown in the results, PCV index can effectively identify the sleeping beauties literature. In addition, compared to the Bcp index, the new method has the advantages of simplicity and accuracy, and further reduces the dependence on the total number of citations.
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