Research on the Construction of Labor Education Curriculum Model Based on Multi-Objective Optimization Algorithm under Fuzzy Cognitive Orientation

Optimization problems usually involve multiple objectives, while fuzzy cognitive maps can effectively show the causal relationship between concepts, and the combination of the two can greatly advance the development of the education field. In this paper, we design a fuzzy cognition-based knowledge map for labor education courses and a multi-objective optimization model for labor education courses to optimize learners’ learning paths and recommend personalized exercises from multiple stages. Through teaching experiments and regression analysis, the teaching effect of the multi-objective optimization algorithm in labor education courses is evaluated. This paper borrows the k-means algorithm to classify learners into four clusters, and the algorithm provides learning path optimization for different clusters of learners in labor education courses. The exercise recommendation accuracy of this paper’s algorithm ranges from 0.91 to 0.97 and has better novelty and diversity recommendation performance. In the experimental class in the fuzzy cognitively oriented multi-objective optimization labor course, the learners’ labor scores improve faster and are about 3.8 points higher than those of the traditional teaching, and the regression results show that this paper’s model has a positive and positive effect on the teaching effect. The average satisfaction scores of this paper’s model in labor education courses for the friendliness of teaching aid, effectiveness of cognitive diagnosis method, usefulness of path optimization, and reasonableness of personalized recommendation of exercises are above 4.3, indicating that the model has practical application value in labor education courses.