Enriching and developing the connotation and value of labor education theories can help students in higher vocational colleges form correct viewpoint and attitude towards labor. Higher vocational colleges should put more efforts to education through practice based on the features of each discipline. Accurately identifying students' behavior in complex practice and training scenarios is very important for teachers to know about their status during practice and training, however, existing research results are not applicable to complex practice and training scenarios since they have neither considered how to improve the accuracy of static image identification while ensuring the model is lightweight structured, nor considered the time series information of students' behavior during practice and training in the collected video images. For this reason, this paper took the property management major as the subject to study the identification of student behavior during practice and training based on video image. In the paper, the students' practice and training content was divided into three aspects, a task of asking students to cooperate with each other to deal with an equipment failure emergency was adopted for the research, and a research idea of helping teachers figure out students' status during practice and training via identifying their actions and intentions during the said activities was determined. Then, a few pre-processing operations were performed on the captured video images of student behavior during practice and training, including removing abnormal image frames, filtering, and aligning, etc. After that, based on the collected video image data, the dynamic convolution kernel was improved and optimized, and a lightweight convolution network model was built for identifying student behavior during practice and training. At last, experimental results verified the validity of the proposed identification model.