摘要：This paper delves into the hidden impact of algorithmic bias on the allocation of online education resources. With the rapid development of online education, algorithms play a crucial role in resource allocation, but algorithmic bias has emerged as a significant issue. The study analyzes the impact of bias at three levels: data level, where data collection and annotation biases lead to uneven resource allocation and misdirected recommendations; algorithmic model level, with design flaws and bias accumulation during optimization causing unfair resource allocation decisions; and result level, imposing implicit restrictions on students’learning opportunities and posing potential threats to educational and social equity. Through case studies of Online Education Platform A and Online Education Project B, the actual manifestations and impacts of algorithmic bias are demonstrated. To address these problems, corresponding countermeasures are proposed, including data governance strategies to improve data quality, algorithmic optimization strategies to enhance fairness and transparency, and educational management and policy recommendations to strengthen regulation and promote algorithmic literacy. This research not only reveals the harm of algorithmic bias but also provides a comprehensive and systematic solution framework, which has important theoretical and practical signifcance for promoting fair resource allocation in online education and realizing educational equity.