A novel unsupervised classification algorithm based immune network was presented. First of all, the formal definitions of antibodies, antigens and immune network were given according to shape space theory, respectively. Afterward, the mathematical models and corresponding equations were established, such that the clonal selection and highfrequency mutation of antibodies, the immunological memory, and etc. Finally, the process of unsupervised classification was presented. The experimental results showed that the algorithm achieves the higher classification accuracy than other traditional clustering algorithms, and has some better characters such that continuous learning, dynamic adjustment, features remembering, and etc. If the antibody is regarded as a given model, and rationalizes the antigens collection, then the model has a wide range of applications.