“Domain-specific text classification often needs more external knowledge, and fraud cases havefewer descriptions. Existing methods usually utilize single-stage deep models to extract semanticfeatures, which is less reusable. To tackle this issue, we propose a two-stage training frameworkbased on within-task pretraining and multi-dimensional semantic enhancement for CCL23-EvalTask 6 (Telecom Network Fraud Case Classification, FCC). Our training framework is dividedinto two stages. First, we pre-train using the training corpus to obtain specific BERT. The seman-tic mining ability of the model is enhanced from the feature space perspective by introducing ad-versarial training and multiple random sampling. The pseudo-labeled data is generated throughthe test data above a certain threshold. Second, pseudo-labeled samples are added to the trainingset for semantic enhancement based on the sample space dimension. We utilize the same back-bone for prediction to obtain the results. Experimental results show that our proposed methodoutperforms the single-stage benchmarks and achieves competitive performance with 0.859259F1. It also performs better in the few-shot patent classification task with 65.160% F1, whichindicates robustness.”