The identification of the membership of the open cluster is the first step to study the nature and to determine the basic physical parameters of the open cluster. It is also important for the research of stellar evolution and the structure of the Milky Way. It is an important research direction in the field of membership identification by using the kinematic information (proper motion, radial velocity) of the star to establish the precise membership identification model. At present,the most widely used models of using kinematic information to identify membership are Sanders'method and nonparametric method. Sanders'method as-sumes both field stars and cluster members distributed according to normal bivariate function, and uses position and proper motion data to calculate every star's membership probability. While nonparametric method makes less restrictive astronomical hypotheses, and allows the multi-use of different variables,such as radial velocities,proper motions,multi-color photom-etry data,etc. In addition,there are some new methods,including multi-dimensional model and clustering algorithm etc,but these new methods still need to be further explored. With the development of astrometry technology,the increasing accuracy of measurement data,and the demand for improving the model accuracy is becoming very urgent. This paper reviews the research background and the status of the membership identification of the star cluster by kinematic methods, and introduces the various algorithms in detail. The advantages and disadvantages of each model are also summarized. Finally,in the coming Gaia era,the future research and development of membership identification models are presented.%对疏散星团进行成员星辨认是研究疏散星团的性质和确定疏散星团基本属性参数的首要步骤,对利用疏散星团开展的恒星演化以及银河系结构等研究方向具有重要意义.利用恒星运动学方面的信息(自行和视向速度)来建立可靠的成员星辨认模型,是成员星辨认领域中一个重要的研究方向.目前利用运动学数据进行成员星辨认较为成熟的模型主要是Vasilevskis-Sanders 的方法和非参数方法.除此之外,还有一些新兴的方法:建立多维模型和聚类算法等,但这些方法还有待于进一步探索.随着天体测量技术的不断发展,测量数据精度不断提高,提高成员星辨认模型可靠性的需求也越来越迫切.综述了用运动学方法对星团进行成员星辨认的研究背景和研究现状,对各类算法进行了简要介绍,总结了每种模型的优缺点.最后,在Gaia时代下,对成员星辨认模型的研究发展进行了展望.
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