The positive significance of ideological and political teaching in colleges and universities based on HOG feature extraction to college students’ innovation and entrepreneurship

Abstract

This paper designs an intelligent ideological teaching model integrating innovation and entrepreneurship education under the guidance of the “6C+6E” theory. In this model, the features are first extracted by the convolution operation of the student face image through the Gabor algorithm. The features extracted by HOG algorithm based on integral graph are concatenated to get the fusion of student face features. Then SVM algorithm is used to classify and recognize the student’s facial features. In the classroom, applying this ideological and political teaching mode, the overall concentration situation of the class reached 0.9988 points at the best time. In contrast, the concentration of the traditional innovation and entrepreneurship teaching classroom were all lower than 0.4 points, and the highest rate of students’ heads up in the overall classroom reached 98.19%. After the implementation of the teaching, there is a significant change in the mean score of students’ awareness of entrepreneurial value (P=0.011<0.05) and understanding of entrepreneurial business opportunities (P=0.016<0.05), which has increased by 50.45% and 36.41% relative to the pre-implementation of the teaching. The Civics teaching model constructed in this paper lays the foundation for the effective development of innovation and entrepreneurship education in colleges and universities and provides a reference basis for the improvement of students’ innovation and entrepreneurship awareness.

Keywords

• hog algorithm
• gabor algorithm
• integral graph
• feature extraction
• innovation and entrepreneurship
• 68t05