In this study, we assess facial recognition technology, including detection, comparison, and attribute analysis, for addressing online education challenges. The system tracks facial expressions and records attendance and emotions, thereby improving educators’ understanding of student performance and aiding in targeted interventions. Future enhancements will include statistical data analysis and seamless data transmission. In summary, facial recognition technology holds promise for enhancing online education, enabling real-time teaching strategy adjustments. In this work, we focus on precise student progress tracking, timely lecture analysis, and adaptive teaching in live web classes. An experimental platform with student, server, and teacher components, utilizing PyramidBox-based face recognition, is developed. Real classroom experiments, including roll call, knowledge review, content explanation, and classroom interaction, reveal face recognition’s positive effect on real-time student monitoring and classroom assessment. This empowers educators to promptly adapt teaching strategies, enhancing lecture quality. The study also highlights emotional states’ impact on learning.