Researches on the audio classification have involved various types of vector features. However, multi-semantics of audio information not only have their own properties, but also have some correlations among them. Whereas, to a certain extent, the simple vector representation cannot represent the multi-semantics and ignore their relations. Tensor Uniform Content Locator (TUCL) was brought forward to express the semantic information of audio, and a three-order Tensor Semantic Space (TSS) was constructed according to the semantic tensor. Tensor Semantic Dispersion (TSD) can aggregate some audio resources with the same semantics, and at the same time, the automatic audio classification can be accomplished by calculating their TSD. And Radical Basis Function Tensor Neural Network (RBFTNN) was constructed and used to train intelligent learning model. For the problem of multi-semantic audio classification, the experimental results show that our method can significantly improve the classification precision in comparison with the typical method of Gaussian Mixture Model (GMM), and the classification precision of RBFTNN model is obviously better than that of Support Vector Machine (SVM).