The full implementation of MOOCs in online education offers new opportunities for integrating multidisciplinary and comprehensive STEM education. It facilitates the alignment between online learning content and learning behaviors. However, it also presents new challenges, such as a high rate of STEM dropouts. Many learners struggle to establish effective learning behaviors and fail to complete the entire learning process. This study focuses on analyzing a vast amount of MOOCs learning behavior instances and proposes a knowledge graph-based analysis and prediction model that is centered around associated STEM concepts. The objective is to provide key insights for fostering sustainable and efficient learning behaviors. By constructing a heterogeneous information network, analyzing and learning from key concepts and their relationships by the improved graph convolution neural networks, and employing adaptive recommendations based on the course-concept matrix, it is possible to generate knowledge graphs from extensive learning behavior instances, both within single course and across multiple courses. This approach accurately captures learning behavior patterns and provides guidance for effective learning paths. The data analysis process and predictive results demonstrate that the knowledge graph can effectively guide learning behaviors and enhance learning outcomes. This research offers technical support and a decision-making foundation for optimizing interdisciplinary learning process within MOOCs for STEM education. It showcases strong innovation and practicality.