# Chinese News Text Classification Method via Key Feature Enhancement
## 1 Introduction  
Web news refers to news documents formed with a network as the carrier. It is different from traditional news content and it brings a brand-new experience to users in terms of transmission channels and reading forms. It can orderly integrate unordered content and greatly compress the amount of information, allowing the user to obtain the most valuable information in the shortest time. In the information world, web news as a mainstream method of information dissemination can be seen everywhere in daily life and a large amount of web news is spread on the internet every day. How to use high-quality text classification technology to help humans to efficiently organize and manage the massive amount of web news is an urgent problem to be solved. Studies have shown that combining high-quality text classification methods can form effective organizational and management capabilities for mass news texts. This popular approach uses feature engineering combined with traditional machine learning methods to complete text classification simply and effectively, but the overall performance is not high. In most scenarios, it is a scientific and effective solution to realize the automatic classification of web news by building a large-scale news annotation dataset and combining deep learning algorithms to train high-quality text classification models [1]. It is worth noting that although such schemes can accurately solve the problem of automatic classification of news text, there are three problems that cannot be ignored: (1) The cost of building a large-scalelabeled dataset is extremely high. As we all know, high-quality labeled data is the core driving force for the development of artificial intelligence, which contributes to improving the performance of text classification algorithms. However, in some low-resource scenarios, it is impossible to completely rely on manual methods to achieve large-scale unlabeled news data labeling tasks. (2) The underlying deep learning method that uses a pre-trained language model to represent features is poorly interpretable. Even deep learning methods that incorporate pre-trained language models have achieved impressive results in feature representation and text classification tasks. The pre-trained language model is a black-box model with a large number of parameters and poor interpretability in the form of distributed vector outputs. In particular, the text classification method combined with transfer learning theory, using the prior knowledge learned by the pre-trained language model, interferes with the overall interpretability of the model in the process of parameter transfer, feature transfer and fine-tuning. (3) Most deep learning models are large in size and low in computing performance. After the existing deep learning model is combined with the pre-trained language model, many additional parameters are introduced to make the model bulky and the vector form corresponding to the language model is a distributed dense vector composed of floating-point values, which reduces the calculation performance of the model. The comparative analysis results of different methods are shown in Table 1 in Appendix A. In summary, it is of great significance to improve existing methods from the aspects of reducing the cost of large-scale labeling datasets, improving the interpretability of models, compressing models' sizes and improving classification performance [2]. By analyzing the deficiencies of existing methods, this paper proposes a key feature-enhanced Chinese news text classification method named KFE-CNN. The KFE-CNN method can effectively reduce the cost of labeling large-scale datasets and achieve high-performance text classification goals. In addition, the KFE-CNN method uses a zero-one binary vector to represent features, which can effectively realize feature quantization and model volume compression. The details of the KFE-CNN method are described below. First, some key features are extracted from the original labeled data through a key feature extraction algorithm.