Knowledge graphs (KGs) play a vital role in intelligent education by offering structured representations of educational content. However, constructing multimodal educational knowledge graphs (EKGs) from diverse open educational resources remains a challenge due to the reliance on costly manual annotations and the lack of multimodal integration. In this work, we propose an automated framework that harnesses the reasoning capabilities of large language models (LLMs) to construct multimodal EKGs from open courses efficiently. In our framework, an Extraction-Verification-Integration-Augmentation pipeline is designed to incrementally extract and refine disciplinary concepts from learning resources. Texts, images, videos and audios are aligned with their corresponding concepts. To ensure semantic consistency across modalities, we propose a cross-modal alignment method based on shared structural and semantic features. Using our framework, we build SciMKG, a large-scale multimodal EKG for Chinese K12 education in sciences (biology, physics, and chemistry), encompassing 1,356 knowledge points, 34,630 multimodal concepts, and 403,400 relational triples. Experimental results show that our method improves concept extraction F1 score by 9 % over state-of-the-art baselines; both automatic and human evaluations confirm the robustness of our multimodal alignment method. SciMKG and our construction toolkit will be publicly released to support further research and applications in AI-driven education.

Thymoma CT images exhibit significant scale variations and blurred boundaries, posing a challenge to automatic segmentation. The proposed model is a multi-scale dual attention and gated skip connection network (MSDACG-Net), which suppresses redundant features in skip connections through context gating (CoT Gate) and enhances context modeling capabilities at high resolution using a multi-scale dual attention aggregation (MSDA) module. The effectiveness of the method was validated through experimentation on a thymoma CT dataset that had been self-built. A comparison of the proposed model with a strong baseline reveals that MSDACG-Net improves Dice by 1.01%, reduces HD95 by 24%, and improves Recall by 0.59%, demonstrating superior overall accuracy, boundary approximation ability, and detection robustness. Moreover, cross-modal experiments on the publicly available polysegmentation dataset Kvasir-SEG demonstrate the proposed method’s capacity for effective generalisation.