Processing facial data in classroom settings is a critical prerequisite for implementing intelligent education technologies. However, traditional single-stage detection methods suffer from low recognition accuracy and high false detection rates due to complex environmental factors such as lighting variations and pose occlusions. Simultaneously, the emotional state information conveyed through students’ microexpressions in classrooms serves as vital feedback for teachers to adjust instructional strategies and enhance teaching quality, necessitating precise data collection and processing for effective extraction. This project proposes a classroom-adapted facial data processing method that enhances detection and screening efficacy under complex conditions while accurately capturing microexpression-based emotional data. This provides high-quality data support for subsequent classroom emotion analysis and teaching quality optimization. Employing a “two-stage Haar cascade detection + geometric feature fusion screening” framework, the method utilizes loose parameter detection to capture potential faces, followed by strict parameter re-inspection and aspect ratio/area threshold filtering to eliminate false positives. This approach effectively counters classroom environmental interference, significantly improving the accuracy and reliability of micro-expression-containing facial data processing. It provides a viable solution for emotion analysis and teaching quality enhancement in intelligent education scenarios.