Exploration and Analysis of the Path Optimization of Labour Recommendation Model Based on Big Data and Auto-Kg Algorithm

Abstract

At present, labour education has a significant status and role in higher education. It not only meets the basic needs of students' social life, but it is also a measure of the comprehensive quality of students. Based on the perspective of big data and system dynamics, the article designs an adaptive college labour education learning system. The system includes domain model, learner model, adaptive model and cognitive diagnosis model. The article uses Auto-kg algorithm to construct a knowledge map of the learning content of labour education in colleges and universities. Design adaptive learning content recommendation strategy based on learner's learning style and realize adaptive recommendation of learning content. The results show that the recommendation model can significantly improve the learning effect of learners.