Service classification of high-speed network traffic is critical for Internet Service Providers (ISPs) to ensure network Quality of Service (QoS). As high-speed network transmission accelerates, ISPs can only obtain unlabeled and sampled traffic from high-speed networks, making supervised learning methods difficult to apply. Some existing methods use unsupervised learning to classify services to reduce the need for labeled data. However, when these methods are applied, fluctuations in the feature vector lead to a certain percentage of the same class of services being grouped into different clusters. We proposes a practical method for classifying traffic services in high-speed networks. Specifically, we propose a method called Two-Stage Clustering (TSC), which automatically implements merging clusters of the same service. Validation experiments on publicly available datasets show that our classifier achieves an accuracy of 90.07% and a recall of 91.81% even with a sampling rate of 1:64, which is higher than the classification methods that also use unsupervised learning.