With the development of SOA and SaaS technologies, the scale of services on the Internet shows a trend of rapid growth. Faced with the abundant and heterogeneous services, how to efficiently and accurately discover user desired services becomes a key issue in service-oriented software engineering. Services clustering is an important technology to facilitate services discovery. However, the existing clustering approaches are only for a single type of service documents, and they do not consider the domain characteristic of services. To avoid these limitations, on the basis of domain-oriented services classification, this paper proposes a services clustering model named as DSCM based on probability and domain characteristic, and then proposes a topic-oriented clustering approach for domain services based on the DSCM model. The proposed clustering approach can cluster services described in WSDL, OWL-S, and text, which can effectively solve the problem of single service document type. Finally, experiments are conducted on real services from ProgrammableWeb to demonstrate the effectiveness of the proposed approach. Experimental results show that the proposed approach can cluster services more accurately. Compared with the approaches of classical latent Dirichlet allocation (LDA) and K-means, the proposed approach can achieve better in the purity of cluster and F-measure, which can greatly promote on demand services discovery and composition.