Construction of a Research Model for Overseas Dissemination of Chinese Literature Based on Deep Learning Models

Introduction

In the era of big data and innovative change, traditional teaching models and methods are facing unprecedented challenges. The explosive growth of information and the transformation of learning methods have driven us to have a new understanding of the education system (Bai, 2020). However, information overload comes with it, and users often find it difficult to extract useful data from a large amount of information (Liu, & Zou, 2023). It is often necessary, especially in supervised learning, to learn a hyperplane that can distinguish different problems, but the hyperplane’s original functionality may not be able to meet this requirement, so finding a suitable feature-mapping space is crucial. As an emerging research direction in the field of machine learning, deep learning has successfully addressed complex and uncertain problems by simulating the human nervous system, and it has achieved significant results in areas such as image classification, text detection, and speech recognition.

More and more contemporary Chinese literary works are going global, providing opportunities for promoting cultural exchange and optimizing the global cultural ecology. However, the literary translation ecology still faces some problems, especially in how to effectively disseminate Chinese literature and its essence. To address these issues, deep learning technology, as a new computational method, has begun to make breakthroughs in multiple fields, especially in understanding complex information and extracting deep meanings. With the development of algorithms, deep learning has become the core of intelligent research and is gradually being applied to emerging fields, such as mobile machine learning. In this article, I aim to summarize and analyze the research on deep learning and the dissemination of Chinese literature, using methods such as establishing calculation formulas and data graphs for analysis and combining interpretation and model drawing. By using these methods, this study not only provides readers with a clear research framework but also further expands the application of deep learning in the field of cultural communication.

The main contributions of this article include establishing vivid model diagrams to demonstrate research results, using multiple evidence to deepen research, and revealing the potential application of deep learning models in the dissemination of Chinese literature. This study not only contributes to the optimization of the literary translation ecosystem but also provides new ideas for the global dissemination of Chinese literature.

I first introduce relevant work and further explain pertinent research at home and abroad. Then, I introduce the relevant theories and research methods related to the dissemination of Chinese literature. Next, I introduce the research results of deep learning models and analyze the data. I conclude the article with a summary.