Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing

The development of Internet technology has led to a rapid increase in news information. Filtering out valuable content from complex information has become an urgent problem that needs to be solved. In view of the shortcomings of traditional manual classification methods that are time-consuming and inefficient, this paper proposes an automatic classification scheme for news texts based on deep learning. This solution achieves efficient classification and management of news texts by introducing advanced machine learning algorithms, especially an optimization model that combines Bi-directional Long Short-Term Memory Network (Bi-LSTM) and Attention Mechanism. Experimental results show that this solution can not only significantly improve the accuracy and timeliness of classification, but also significantly reduce the need for manual intervention. It has important practical significance for improving the information processing capabilities of the news industry and accelerating the speed of information flow. Through comparative analysis of multiple common models, the effectiveness and advancement of the proposed method are proved, laying a solid foundation for future news text classification research.

I. Introduction

With the rapid development and popularization of Internet technology, news information on the Internet has shown an explosive growth trend, allowing people to easily obtain various news information from the Internet. However, this also brings certain challenges, namely how to extract valuable content from the vast amount of online news. The news industry plays a vital role in economyand is closely related to people's lives. Due to the suddenness and urgency of news, it is also necessary to manage the causes and categories of subsequent news in a detailed manner. Therefore, in order to effectively organize and manage online news information, text classification technology is introduced to effectively organize and manage news text information [1]. This method can divide text into different categories according to its content, thereby avoiding information confusion and helping to extract more valuable content.