## 5 Conclusions  
This paper proposes a new text classification model. As a fundamental task in natural language processing, text classification finds wide applications in various domains, including topic identification in social media, sentiment analysis and opinion mining, content and comment analysis; categorizing articles, blogs, and editorials in the news domain; financial sentiment analysis, market prediction, credit assessment, fraud detection in the financial domain; medical literature classification, disease diagnosis, adverse drug event detection in the medical and biological domain; as well as automated customer service and user support systems in the customer service domain. These are just a few examples of the practical applications of text classification models. In reality, text classification techniques have important applications and impacts in many fields. The purpose of building this new model is to apply it across industries and enhance work efficiency.  
We propose a multi-layer feature fusion text classification model called CAC, which combines the advantages of CNN and Attention. This integration enhances the performance of text classification by leveraging the strengths of both approaches. Our main contributions include:  
1. We propose a novel text classification model that combines CNN and Attention for parallel feature extraction. This architecture fully utilizes the advantages of CNN in local feature extraction and Attention in global attention calculation, thereby enhancing the model's feature extraction capability.
2. We adopt a \"local-first, global-later\" approach, preserving the sequential information of the text during the feature extraction process and capturing semantic relationships at different levels in the text. Through this approach, our model gains a better understanding of the text's semantics, thereby improving text classification performance.
3. Extensive experiments have demonstrated that our model achieves outstanding performance on multiple datasets. Compared to methods based on CNN, RNN, and attention models, our model has shown its effectiveness and superiority in terms of accuracy and performance in text classification tasks.  
This study successfully applies the proposed architecture to the field of natural language text processing. However, there are still many research directions worth exploring in the future. Since the model incorporates CNN modules, we can further investigate the advantages and potential applications of this model structure in image processing. For example, it may be applicable in image classification, object recognition, object detection, and other areas. By combining the fields of text processing and image processing, we can explore more cross-domain applications. For instance, we can attempt multimodal tasks using our proposed model structure, such as joint analysis and understanding of images and text.  
Conceptualization, H.Y. and S.Z.; methodology, G.Z.; software, S.Z. and L.F.; validation, H.Y., J.W. and S.Z.; formal analysis, S.Z.; investigation, H.S.; resources, H.Y. and H.S.; data curation, H.S.; writing--original draft, H.Y. and S.Z.; writing--review and editing, X.D., J.X., S.S. and H.Z.; visualization, S.Z.; supervision, H.Y.; project administration, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the School Enterprise Cooperation Project (No.whpu-2021-kj-762 and 1145, No.whpu-2022-kj-1586 and 2153), the Hubei Provincial Teaching and Research Project (No.2018368), and the Ministry of Education Industry-University Cooperation Collaborative Education Project (No.220900786024216).  
Not applicable.