## 1 Introduction
### Contributions  
In this work, we aim to address the three challenges mentioned above in the task of categorizing topics. Inspired by the above two key insights, we propose a method based on a residual-connected Transformer encoder and an overall semantic perception approach named RQ-OSPTrans. Specifically, this method preserves a copy for the input of each layer of the encoder, maximizing the retention of all features of the text sequence and allowing the model to relearn to increase the stability in classification. The preserved copies serve as a \"re-questioning\" of the previous round of learning results as the network deepens, requiringthe model to more thoroughly relearn and provide classification predictions. Moreover, with the aid of feature space normalization, this model maintains a stable distribution of features in the input's feature space, enhancing the precision of attention mechanism learning and the determinism of feature partitioning in the multi-layer perceptron (MLP). In the BERT [1] output structure, an overall semantic perception module based on the global semantic pooling expression is applied to learn global semantic features from high-dimensional data and guide the classification output. Finally, a \(\lambda\) factor is introduced to weigh the sum of the classification and guided outputs, combined with Softmax weights, ensuring that the model's output fully considers the capabilities of each learner and the contribution of the different semantics to the classification.  
The main contributions of this work can be summarized as follows:  
1. This work proposes a method based on a repeat question module and an overall semantic perception approach named RQ-OSPTrans to address the challenges in long-text classification with noise. The model achieves a more effective long-text classification by combining non-adjacent sequence question repetition learning with overall semantic perception.
2. This work establishes a dual-classification learning mechanism through parallel networks to compute the classification and guided outputs separately for annotating labels on a given text segment. A semantic correction mechanism, amplified by Softmax weights, is introduced to enhance the accuracy of identifying textual knowledge points and detecting moral values through learning non-adjacent features in long sequences.
3. RQ-OSPTrans is validated to be competent in Chinese text topic recognition and derived semantic detection in most scenarios through extensive experiments on the self-built datasets CIPCC, Chinese topic recognition datasets, and publicly available English datasets. This mechanism performs at par with state-of-the-art methods in English tasks.  
The remaining sections of this paper are structured as follows: Section 2 introduces related work, including deep-learning methods and pre-trained methods based on the Transformer. Section 3 describes the detailed structure and network propagation logic of RQ-OSPTrans, comprising word sequence embedding learning, repeat question modules, and overall semantic perception modules. Section 4 details the dataset selection and training results. Section 5 concludes our work.