With the rapid development of artificial intelligence and deep learning in recent years, many universities have put forward the goal of achieving digitalization, intelligent, and education informatization on campus. Throughout the lecture and learning process, the classroom status is an important reference factor to assess students’ acceptance of the course and the quality of lectures. However, at present, classroom status analysis is mainly conducted manually, which can distract teachers’ attention, so it is of great research significance to find a method that can improve the efficiency of classroom status analysis. In this paper, we choose an offline method to analyze the status of a classroom video recording in terms of students’ behavior and attendance in terms of frames, in which student behavior is identified by an improved target detection algorithm and attendance is analyzed by face recognition. By analyzing the structure of the neural network model, an improved neural network model is proposed for its characteristics of a large number of parameters and poor detection of small targets in the basic network. The backbone network is replaced by the improved neural network, and the depth-separable convolutional network is used to reduce the network parameters and increase the computation speed. The information in the deeper feature map is fused upward into the shallow layer to improve the accuracy of small target recognition. Finally, the optimization algorithm is incorporated into the network to optimize the network model and accelerate the model convergence speed. In addition, this paper incorporates the improved behavior recognition method and face recognition method into the system to realize the analysis of the offline classroom status. The system is divided into a teacher side and a management side, where the teacher side is responsible for uploading course recordings and the management side is responsible for randomly analyzing students’ status and attendance at any time, and the combination of the two forms a convenient and comprehensive classroom status analysis system platform. Users can upload classroom videos through the instructor interface and can view the classroom status analysis results of a course at any time by searching randomly in the administration. In this paper, the classroom status is mainly judged by the recognition of students’ behaviors.

中文翻译：

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基于大数据分析的神经网络拓扑构建与课堂交互基准图

近年来，随着人工智能和深度学习的飞速发展，许多高校提出了实现校园数字化、智能化、教育信息化的目标。在整个讲课和学习过程中，课堂状态是评估学生对课程的接受程度和讲课质量的重要参考因素。但目前课堂状态分析主要是手工进行，容易分散教师的注意力，因此寻找一种能够提高课堂状态分析效率的方法具有重要的研究意义。在本文中，我们选择离线方法来分析课堂视频录制的学生行为和出勤状态，以帧为单位，其中通过改进的目标检测算法识别学生行为，并通过面部识别分析出勤情况。通过分析神经网络模型的结构，针对基本网络中参数多、小目标检测能力差的特点，提出了一种改进的神经网络模型。将骨干网络替换为改进的神经网络，使用深度可分离的卷积网络来减少网络参数，提高计算速度。将较深的特征图中的信息向上融合到浅层，以提高小目标识别的准确性。最后将优化算法融入网络，优化网络模型，加快模型收敛速度。此外，本文将改进后的行为识别方法和人脸识别方法结合到系统中，实现对线下课堂状态的分析。系统分为教师端和管理端，教师端负责上传课程录音，管理端负责随时随机分析学生的状态和出勤情况，两者结合形成方便和综合课堂状态分析系统平台。用户可以通过教师界面上传课堂视频，并可以在管理中任意搜索，随时查看课程的课堂状态分析结果。本文主要通过对学生行为的认可来判断课堂状况。