## 5 Conclusions  
The work proposes a text classification method, RQ-OSPTrans, based on the residual-connected Transformer encoder and parallel networks to address the challenge of accurately identifying topics in long texts containing colloquial expressions and noise. The experimental results demonstrate that our method achieves outstanding performance in topic recognition for texts longer than or equal to 256 tokens. Specifically, we achieve validation accuracies of 98.5%, 94.8%, and 84.4% on the THUCNews, AG's News, and arXiv-10 datasets, respectively. These results indicate a significant performance improvement over state-of-the-art pre-trained models in Chinese tasks, while also demonstrating competitiveness in English tasks. However, an important part of RQ-OSPTrans' capabilities comes from pre-trained models, which results in it being more computationally expensive than deep-learning models for other topic classification tasks. In future research, we aim to endow RQ-OSPTrans with multimodal recognition capabilities by adjusting the input-embedding module and improving the accuracy of the domain dataset. One possible approach is to replace the BERT model with the backbone network of the Swin-transformers family, so that RQ-OSPTrans can improve some domain-specific classification problems on image classification tasks, such as the CUB-2011-200 bird fine-grained classification task. This strategy may allow RQ-OSPTrans to be used as an auxiliary model for LLMs, revealing the emergent power that can be brought about by high computational costs. In addition, we will also try to continue to train RQ-OSPTrans on automatic review application data such as social media speech dataset and sentiment analysis dataset, and explore the performance improvement of multiple text classification tasks under the same computing cost, so as to improve the data review efficiency of related industries.  
Conceptualization, Y.T., Q.L. and Z.C.; methodology, Y.T. and Z.C.; validation, Y.T. and Q.L.; formal analysis, Y.T., Z.C. and H.L.; investigation, Y.T, Z.C. and H.L.; resources, Y.T. and Z.C.; data curation, Y.T., Q.L., Z.C. and H.L.; writing--original draft preparation, Y.T. and Q.L.; writing--review and editing, Y.T., Q.L., Z.C. and H.L.; supervision, Z.C., T.L. and H.L.; project administration, Y.T. and Z.C.; funding acquisition, Z.C., H.L., T.L. and S.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (grant No. 62277026), the Research Project of National Collaborative Innovation Experimental Base for Teacher Development of Central China Normal University (grant No. CCNUTEIII 2021-21), the National Natural Science Foundation of China (grant 62277041, grant 62211530433, grant 62177018), and in part by the National Natural Science Foundation of Hubei Province project (No. 2022 CFB529, 2022 CFB971), the Jiangxi Provincial Natural Science Foundation under Grant (No. 20232 BAB212026), the University Teaching Reform Research Project of Jiangxi Province (Grant No. JXJG-23-27-6), and the Shenzhen Science and Technology Program under Grant (No. JCYJ20230807152900001).  
Not applicable.  
**Data Availability Statement:** The dataset used in this article are available upon request from the corresponding author (For academic research use only). The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.