Student classroom behavior recognition is important for improving the quality of teaching and learning. Most of the current mainstream research is based on video or sensor technologies, however, these methods suffer from privacy invasion and high cost, which constrain their wide application. Therefore, this paper proposes a method of student classroom behavior recognition based on WiFi CSI. This method first collected CSI signals for four typical classroom behaviors (hands up、stand up、sit down and turn over books) in a real classroom environment. Then combined with the characteristics of WiFi CSI data, Hampel filter and wavelet transform are used to denoise the CSI signal, and all subcarrier features are fused by designing principal component analysis algorithm. Subsequently, the CSI action intervals are intercepted according to the fusion features designing the local outlier factor detection algorithm, and the intercepted CSI signals are converted into amplitude energy maps by introducing three-dimensional mapping. Finally, the amplitude energy map dataset is feature extracted and classification recognized by designing a transfer learning model based on residual network. The experimental results show that the accuracy of the method is 98.89% and 99.07% in the step classroom and small classroom, and it can reach more than 98% in the test for different people. It is proved that the method has high recognition accuracy and good robustness, which provides a new idea for the research of student classroom behavior recognition.