In order to solve the problem that it is difficult to obtain a composite service with high overall performance in a large-scale Web service environment， a large-scale Web service composition method was proposed. Firstly， Document Object Model （DOM） was used to parse the user demand document in XML format to generate an abstract Web service composition sequence. Secondly， the service topic model was used for service filtering， and Top-k specific Web services were selected for each abstract Web service to reduce the composition space. Thirdly， in order to improve the quality and efficiency of service composition， an Optimized Grey Wolf Optimizer based on Logistic chaotic map and Nonlinear convergence factor （OGWO/LN） was proposed to select the optimal service composition plan. In this algorithm， chaotic map was used to generate the initial population for increasing the diversity of service composition plans and avoiding multiple local optimizations. At the same time， a nonlinear convergence factor was proposed to improve the optimization performance of the algorithm by adjusting the algorithm search ability. Finally， OGWO/LN was realized in a parallel way by MapReduce framework. Experimental results on real datasets show that compared with algorithms such as IFOA4WSC （Improved Fruit Fly Optimization Algorithm for Web Service Composition）， MR-IDPSO （MapReduce based on Improved Discrete Particle Swarm Optimization） and MR-GA （MapReduce based on Genetic Algorithm）， the proposed algorithm has the average fitness value increased by 8.69%， 7.94% and 12.25% respectively， and has better optimization performance and stability in solving the problem of large-scale Web service composition.