Research on Network Resources Integration of University English Blended Learning Model Based on Multi-Objective Optimization

This paper adopts multi-objective optimization technology to solve the problems of transmission and processing of network resource integration in the blended learning mode of university English and realize efficient grouping to avoid the idle and waste of teaching resources. Based on the integration of network resources, a multi-objective optimization model for intelligent grouping of English exams is constructed, and the objective optimization function for intelligent grouping of English exams is established. Formulate the grouping principles and constraints and use the improved genetic algorithm to complete the optimal solution for the model grouping in this paper. In the simulation experiments of English grouping, the model of this paper and the improved genetic algorithm greatly improve the execution efficiency of the intelligent grouping task and overcome the defects of the standard genetic algorithm. In further English teaching practice, the experimental class using this paper’s model for assisted teaching is 9.4 points higher than the control class in the posttest, showing a significant difference (P=0.016<0.05). Meanwhile, the number of students in the high score ranges of 120-150 and 110-119 increased, the number of students in the low score range decreased, and the number of students in the upper intermediate level peaked.