This paper uses deep learning algorithms to detect and extract student behavioral features in the Civics classroom. It first predicts and detects the target location and behavior of students in classroom images by FastR-CNN algorithm, extracts the detected student data features by MSResNet-50 algorithm, and then introduces the attention mechanism to screen the extracted behavioral features, and analyzes the accurate location information and channel information to get the student’s behavioral performance in the Civics classroom. The Civics element framework is used to construct a digital transformation path for Civics courses in this study. In order to reflect the value of the digital transformation of Civics teaching, the study conducted a teaching comparison practice. After a semester’s test, the Civics scores of the students in the five classes were above 80 (good or excellent). In contrast, the mean score of Civic Literacy in the post-test reached 3.863, which was significantly different from that before the practice (p=0.007<0.05). The value of “student-initiated speaking” behavior was higher than 45 in the practice class, and the student’s participation in the class was much higher than that in the control class. Analysis of the effect of teaching implementation found that the motivation score of students in class E increased from 3.511 to 4.957, with a significant difference (p=0.048<0.05). It was also found that the posttest score of “value concept” of students’ Civics literacy increased by 21.19%, and the score of “honesty and friendliness” also increased by 0.959 points. The massive amount of digital information generated by the implementation of the digital Civics teaching path in this paper may affect the student’s ability to think independently. However, the results achieved by the teaching path still provide reference ideas for the innovation of Civics teaching and reference data for the value and significance of the path’s implementation.