Toxic language detection is an important task for online content moderation. However, existing models often associate neutral group-related terms—such as identity, regional, or demographic expressions—with toxic labels, producing false-positive bias in which benign texts containing such terms are misclassified as toxic. To address this, we propose NISC, a neuron-level iterative debiasing and semantic compensation framework for Chinese toxic language detection. Starting from a trained detector, NISC first identifies bias sources from high-confidence false-positive samples without a manually predefined lexicon. It then builds paired samples with and without each bias source and compares neuron-level toxic contributions between them. Guided by this contribution difference, NISC locates neurons that drive false-positive predictions and weakens them through iterative pruning. To offset the semantic degradation that pruning may cause, NISC uses a large language model to generate benign non-toxic samples that contain the identified bias sources but express no attack, insult, discrimination, or hatred, and applies them for lightweight compensation training with the pruning mask fixed. Experiments on multiple Chinese toxic language datasets show that NISC lowers the false-positive rate on group-related neutral texts while largely preserving conventional detection performance.

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.