Structural domains in proteins are the basic units to form various proteins. In the protein's evolution and functioning, domains play important roles. But the definition of domain is not yet precisely given, and the update cycle of structural domain databases is long. The automatic algorithms identify domains slowly, while protein entities with great structural complexity are on the rise. Here, we present a method which recognizes the compact and modular segments of polypeptide chains to identify structural domains, and contrast some data sets to illuminate their effect. The method combines support vector machine (SVM) with K-means algorithm. It is faster and more stable than most current algorithms and performs better. It also indicates that when proteins are presented as some Alpha-carbon atoms in 3D space, it is feasible to identify structural domains by the spatially structural properties. We have developed a web-server, which would be helpful in identification of structural domains (http://vis.sculab.org/~huayongpan/cgi-bin/domainAssignment.cgi).