This paper presents an enhanced artificial lemming algorithm (EALA) for solving complex unmanned aircraft system (UAV) path planning problems in three-dimensional environments. Key improvements include chaotic initialization, adaptive perturbation, and hybrid mutation, enabling a better exploration–exploitation balance and local refinement. Validation on the IEEE CEC2017 and CEC2022 benchmark functions demonstrates the EALA’s superior performance, achieving faster convergence and better algorithm performance compared to the standard ALA and 10 other algorithms. When applied to UAV path planning in large- and medium-scale environments with realistic obstacle constraints, the EALA generates Pareto-optimal paths that minimize length, curvature, and computation time while guaranteeing collision avoidance. Benchmark tests and realistic simulations show that the EALA outperforms 10 algorithms. This method is particularly suited for mission-critical applications with strict safety and time constraints.

With the advancement of digital transformation in education, human–computer interaction technology, especially facial expression recognition technology, has attracted much attention in education. This study focuses on applying facial expression recognition in computer vision for classroom teaching evaluation, with the aim of providing new ideas for improving teaching quality. This method uses the YOLOv8 network model to detect classroom facial data in real-time and extract key features of students’ facial expressions in class, accurately identifying expressions such as happiness, anger, neutral, and sadness. Subsequently, the head-up rate, facial expression score, and emotion category of students in the classroom were calculated separately to analyze their classroom focus, participation, and classroom emotions. This method can reflect students’ learning status, help teachers detect emotions, grasp learning situations, optimize teaching content, enhance teacher-student interaction, and solve problems such as lagging evaluation, strong subjectivity, and neglect of students’ emotional feedback in traditional classroom teaching. The research results indicate that this method can serve as an important reference for classroom teaching evaluation, promote high-quality development of classroom teaching, inject new vitality into the intelligent and precise development of education, and has broad application prospects.