Sustainable Development Goals (SDG) 4.7 aims to ensure learners acquire the knowledge and skills for promoting sustainable development by 2030. Yet, Open Educational Resources (OERs) that connect the public with SDGs are currently limitedly assigned and insufficient to promote SDG and sustainability education to support the achievement of SDG 4.7 and other SDGs by 2030, indicating a need for automatic classification of SDG-related OERs. However, most existing labeling systems can not support multiple labeling, tend to generate a large number of false positives, and have poor transferability within the OER domain. This research proposes a method to automatically assign SDGs based on AutoGluon, a machine-learning framework with powerful predictive capabilities, to allow multiple SDGs to be assigned to each OER. In the proposed framework, challenges of category imbalance and limited data availability are addressed, enhancing the precision and applicability of SDG integration in educational resources. To validate the transferability of model knowledge within the OER corpus, we used 900 lecture video descriptions from SDG Academy, forming the foundation for comparing our framework with existing labeling systems. According to the experiment results, our model demonstrates outstanding merits across various metrics, including precision, recall, F1, ACC, AUC, and AP.