In complex systems (e.g., communication, transportation, and biological networks), high robustness ensures sustained functionality and stability even when resisting attacks. However, the inherent structure complexity and the unpredictability of attacks make robustness optimization challenging. Hypergraphs provide a framework for modeling complicated higher-order interactions in complex systems naturally, but their potential has not been systematically investigated. Therefore, we propose an effective method based on genetic algorithms from Artificial Intelligence to optimize the robustness of complex systems modeled by hypergraphs. By integrating percolation-based metrics with adaptive computational techniques, our method achieves improved accuracy and efficiency. Experiments on both synthetic and real-world hypergraphs demonstrate the effectiveness of the proposed method in mitigating malicious attacks, with robustness improvements ranging from 16.6% to 205.2%. Further in-depth analysis reveals that optimized hypergraph-based systems exhibit a preferential connection mechanism in which high-hyperdegree nodes preferentially connect to lower-cardinality hyperedges, forming a distinctive Lotus topology that significantly improves robustness. Based on this finding, we propose a robust hypergraph generation method that allows robustness to be controlled via a single parameter rb. Notably, for rb<-1, a distinct Cactus topology emerges as an alternative to the Lotus topology observed for rb>1. The discovery of the Lotus and Cactus topologies offers valuable insights for designing robust higher-order networks while providing a useful foundation for investigating cascading failure dynamics in complex systems.

Accurately modeling learners’ course preferences is a key task in Massive Open Online Course (MOOC) recommendation. Existing sequential recommendation methods can capture recent changes in learners’ interests but have limited ability to exploit shared preference patterns across learners. In contrast, graph-based recommendation methods can capture collaborative information from learner–course interactions, but they often ignore the effects of behavior order and time intervals on interest evolution. To address these problems, this study proposes Sequence Graph Fusion Recommendation (SGFRec), a method for MOOC course recommendation. SGFRec models learners’ recent interests from course-taking sequences and collaborative preferences from learner–course interactions. It further considers the time intervals between course interactions to distinguish different learning rhythms. An adaptive fusion mechanism then balances recent individual interests and collaborative information for each learner. Experimental results on the MOOCCube and MOOCCourse datasets show that SGFRec outperforms several baselines, demonstrating improved offline recommendation ranking performance.