Research on Semantic Network Construction and Critical Discourse Analysis Methods in the Context of Big Data

This study explores the integration of Critical Discourse Analysis (CDA) and big data technology in the evaluation and enhancement of college English teaching. By utilizing the K-nearest neighbor (KNN) classification algorithm with K-value optimization, students' assessments of learning value, teaching enthusiasm, classroom organization, interaction, and teacher-student relationships were systematically analyzed. The findings revealed that students place greater emphasis on cognitive benefits, such as intellectual stimulation (mean score of 4.35) and enhanced insight (3.81), while cultural understanding (3.34) and self-confidence (3.14) are perceived as less important. Teacher encouragement significantly contributes to promoting classroom participation (3.38), though there remains room for improvement in fostering teacher friendliness and support. The study underscores the importance of enhancing the student experience to facilitate deeper learning and greater equity. Additionally, it highlights the need for the modernization of college English education through the application of big data insights and a critical discourse framework, aiming to foster students' comprehensive development, integrate cultural understanding, improve communication skills, and better prepare them for the challenges posed by globalization and increasingly complex environments.