Abstract: Based on the current increase in the mass of Web services, the inefficiency of existing Web service selection algorithms, and poor user matching, this paper proposes a solution to the problems of particle shift, low accuracy, and easy distortion in the K-medoids point algorithm. Research on Web service composition based on K-medoids point optimization algorithm is given in big data environment. The method is based on the study of Web service selection and optimal Web service combination based on the K-medoids point algorithm that optimizes the initial clustering center based on the satisfaction of different user needs and the QoS parameters of Web services in a big data environment. At the same time, the accuracy of dynamic selection and combination of services, the number of iteration updates, the selection time of candidate sets and the total selection time are experimentally analyzed for different selection methods, which verifies the effectiveness and reliability of the method in this paper.

Key words: match； big data, K-medoids； service composition