Decision Support Algorithm for Discipline Construction of Comparative Pedagogy Based on Evolutionary Graph Data Mining

The traditional pedagogy discipline construction decision support algorithm has the problems of poor discipline construction decision satisfaction, high decision time, and low decision recall rate. Therefore, a decision support algorithm for discipline construction of comparative pedagogy based on evolutionary graph data mining is designed. First, the programme call graph is created based on the programme execution path, and then the graph reduction method is used to decrease the call graph set and create the weighted behavior graph set. The original graph set is then reduced using the subtree reduction procedure. The interference of weights in the graph must be eliminated while mining closed subgraphs using a close graph method. The most frequent subgraph of comparative pedagogy discipline construction is then mined, and an SVM classifier is created to accomplish information mining of comparative pedagogy discipline construction in evolutionary graph data mining. Then, using the complete weighing approach, the attribute classification of comparative pedagogy discipline construction is accomplished, and the decision weight of comparative pedagogy discipline construction is established. Finally, the weight distribution scheme of comparative pedagogy discipline construction is obtained by using European distance, so as to realize the decision support of comparative pedagogy discipline construction. The experimental results show that the decision-making satisfaction of comparative pedagogy discipline construction of this method is 97.32%, the decision-making time is only 0.9 min, and the decision-making recall rate is as high as 98.66%, indicating that the decision-making effect of comparative pedagogy discipline construction of this method is good.