Construction and application of a high-quality smart classroom education simulation platform in a cloud system environment

With the continuous promotion of China’s education informatization reform, classroom construction is gradually moving towards the direction of intelligence and personalization. In the era of Internet plus, the smart classroom model with talent training as the core and computer technology as the driving force has gradually improved. But currently, the evaluation of offline classrooms in smart classrooms relies too heavily on subjective evaluation, making it difficult to fully quantify classroom effectiveness. Therefore, the research aims to improve the teaching effectiveness of classrooms through real-time monitoring and intelligent device regulation, and carefully and accurately quantify evaluations. The research adopts improved background frame difference object detection algorithm and object tracking algorithm for moving object detection and tracking. Meanwhile, a real-time facial recognition method based on visual tracking has achieved student micro-expression recognition and focus assessment. The focus evaluation method is based on the three-dimensional learning state space and emotional dimension theory. Building a smart classroom system on this basis, which includes a monitoring system, recording and tracking positioning system, and evaluation system. This system can achieve teaching monitoring for students, recording and saving, and tracking, positioning, and quantitative evaluation of teaching students. These results confirm that the accuracy of the system is maintained at 96%. In the testing of tracking and positioning algorithms, the background difference method has the highest accuracy, reaching 90.2%. In the facial expression recognition experiment, the recognition rate of happy, expressionless, confused, and surprised expressions is relatively high, but the recognition rate of suppressed expressions needs to be improved. As a result, the intelligent classroom system designed through research has performed excellently in data stability, system performance, tracking and positioning algorithms, and teaching evaluation. It can provide important references for optimizing smart classroom education management systems in the future.