The nine-component cross-correlation function(NCF)has been paid more and more attention to with the development of research based on ambient noise cross-correlation. However,with the development of large aperture and dense arrays,it is challenging to quickly calculate the cross-correlation function from large-amount dataset in traditional high performance workstations,especially for the nine-component cross-correlation functions which will roughly take nine times longer compared to the vertical-vertical component alone. In present paper we propose one possible solution to speed the calculation of nine-component cross-correlation functions for large dataset using the cloud computing. The cloud computing can provide scalable computation power and storage which is suitable for data intensive computing tasks,while calculating NCFs from large amount data is exactly one data intensive computation. Based on the cloud services provided by Aliyun,we have developed one framework which could factorize the entire computation into small pieces and execute each piece in one single virtual server evoked at the cloud end. Since all those virtual servers can run simultaneously,the time cost to obtain NCFs from large dataset could be highly reduced,which is roughly inversely proportional to the number of evoked virtual servers. We apply this technique to obtain the nine-component NCFs based on the continuous three component records of China Array from 2014 to 2015,which consists of 674 broadband stations and covers a ten by ten degree area in northeast Tibet. Our results show that the entail computation can be finished in eleven hours,which is about 400 times faster compared to that on one single traditional server. We further validated the resulting NCFs by calculating the Rayleigh wave ZH ratios from both the stacked nine-component NCFs and earthquake results and the results suggest our computation method is fast and reliable. Seismology is developing in an era of big data and our study suggests that by utilizing the techniques from computing science of mass data,we can benefit from the advances in observational capabilities.