Identifying self-admitted technical debt (SATD) plays an important role in maintaining software stability and improving software quality. Although existing methods can detect SATD and researchers have identified design debt and requirement debt, an approach to realize multiple classification of SATD, including defect, test, and documentation, is still lacking. In this paper, we combine text generation oversampling and the Convolutional Neural Networks-Gated Recurrent Unit (CNNGRU) model, and propose an approach called SCGRU to classify multiple debt, including defect, test, documentation, design, and requirement. First, SeqGAN-based text generation is employed to generate new samples by learning the original SATD data, thereby increasing the number of SATD samples such as defect debt and reducing data imbalance. Then, we apply the CNNGRU model to refine SATD into multiple classes. An experiment with cross-project identification of 10 projects shows that our approach is more effective than existing methods such as CNN and text mining. The proposed SCGRU approach has strong advantages especially in cases of flawed debt with very unbalanced data such as test debt and documention debt.