In the internet-based 'remote classroom', it is a challenging scientific problem to establish an evaluation correlation for the lecture-listening state between teacher and students based on video data of student classroom status. In this paper, a module was proposed firstly to identify and classify the classroom behaviors of students according to their 'facial posture angle' and 'body movement behavior' in 'remote classroom'. And then, a quantitative evaluation algorithm was proposed based on student facial gesture angle and behavior classification results to analyze quantitatively the student attentiveness. Finally, using evidence theory to carry out the data fusion for student facial gestures and behavioral classification results in parallel, an automatic assessment system model was established to analyze automatically the student on-line concentration in remote classroom. The results show that the proposed model can detect and analyze student listening behaviors, complete score quantitatively and output the evaluation results for student concentration. In concentration assessment experiments, the accuracy of the system can reach 90.4%, verifying the effectiveness of the system.