Analyzing and learning students' behavior states in classroom plays a positive role in understanding and improving the teaching effectiveness. Meanwhile, the application of lightweight network to pattern recognition has become a trend with the development of mobile networks. In order to improve the recognition accuracy of the lightweight network model MobileNetV2 and reduce the computational cost and delay caused by extracting rich features, an improved lightweight network model based on MobileNetV2 is proposed, in which an improved reverse residual module (C-Inverted residual block) is applied to replace the traditional module. In the improved reverse residual module, channel split operation is added to reduce MAC, and channel shuffle operations are used to promote information exchange and channel fusion. Experiments were carried out on Pascal VOC 2007 detection data set to test the general performance of the proposed improved model. Under the operation limits of 140 MFLOPS, 40 MFLOPS and 20 MFLOPS, mean average precision (mAP) of the improved MobileNetV2 algorithm increased by 1.2%, 2.2% and 4.3% compared with MobileNetV2. While the recognition accuracy of the proposed network model on self-made dataset of student classroom behavior states is 4.6% and 3.7% higher than that of MobileNetV1 and MobileNetV2 respectively, and the average recognition rate of students' classroom behavior states can be up to 92.7%. The results of this research combined with mobile networks would be expected to be used to evaluate teaching and learning effects and promote teaching quality improvement.

分析和学习学生在课堂上的行为状态对于理解和提高教学效果起着积极的作用。同时，随着移动网络的发展，将轻量级网络应用于模式识别已成为一种趋势。为了提高轻量级网络模型MobileNetV2的识别精度，减少提取丰富特征导致的计算成本和时延，提出了一种基于MobileNetV2的改进型轻量级网络模型。 ）用于替换传统模块。在改进的反向残差模块中，添加了信道拆分操作以减少MAC，并且使用信道混洗操作来促进信息交换和信道融合。在Pascal VOC 2007检测数据集上进行了实验，以测试提出的改进模型的总体性能。在140 MFLOPS，40 MFLOPS和20 MFLOPS的操作限制下，改进的MobileNetV2算法的平均平均精度（mAP）与MobileNetV2相比分别提高了1.2％，2.2％和4.3％。所建立的网络模型在自制的学生课堂行为状态数据集上的识别准确度分别比MobileNetV1和MobileNetV2分别高4.6％和3.7％，并且学生课堂行为状态的平均识别率可以达到92.7 ％。这项研究的结果与移动网络相结合，有望用于评估教学效果和促进教学质量的提高。