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Service composition is one of the main behavior in the SOC(Service-Oriented Computing) process, which direct and indirect influences effectiveness and precision of service computing; But at present, relation researches mainly focus on semantics recognition and QoS(Quality of Service). In the paper, according to semantics characteristic classification, we proposed a semantics web service characteristic composition approach based on particle swarm optimization, and set up a characteristic selsection mechanism of semantics web service, and adopt charecteristic distance relation to implement service characteristic classification, and use the distance relation to build characteristic tendency degree, sufficiency and characteristic extractor computing formula of semantics web service, at the same time, according to the formula, to implement service characteristic composition algorithm. Then, we set up a optimal mathematical model via characteristic extractor formula. And employ particle swarm to optimize the model and Amazon service set to make experiment, which showed that it is feasible and effective.

All kinds of information systems in service-oriented have been applied many relation fields, Therefore, according to service-oriented component, we proposed a component composition model and algorithm, and established a component composition model oriented service, which employ a immune genetic ant colony algorithm to solve. Finally, make experiments showed that have improved efficiency of component composition.

In view of the semantic questions of the WSDL in describing service componentpsilas discovering, matching, choosing and so on. an automata based on Ontology is proposed to implement the semantic recognition service, which can finally meet the request of service component. Firstly Analysis of OWL-S and WSMO based on Ontology is made, then the semantic Web service component antomata is established. Finally, under the support of WSMO this theory is applied in the information system of Aba Prefecture. The practice indicates that the automata effectively resolved the service component flow modelling and description in the semantics recognition.