Enhancing Ideological and Political Education Through Advanced AI Applications: A Focus on Chinese Word Segmentation and Unsupervised Learning Algorithms

Abstract

Artificial intelligence (AI) has revolutionized various sectors, including higher education, particularly in the domain of ideological and political education. This paper explores how AI technologies can be leveraged to enhance educational practices by creating detailed student profiles for personalized teaching strategies, supporting psychological and ideological development. The authors focus on two key areas: the application of optimized N-gram language models for Chinese word segmentation and the design of loss functions for unsupervised learning algorithms used in image depth estimation. Through rigorous training and optimization, these techniques achieve high accuracy and efficiency in handling complex Chinese texts, thereby facilitating deeper content understanding and enabling advanced natural language processing tasks essential for ideological and political education. The proposed methods are validated using extensive datasets, demonstrating significant improvements in model convergence and system performance.