Journal of Information & Systems Management Jinbo Li 15 4 2025 https://doi.org/10.6025/jism/2025/15/4/169-176 https://www.dline.info/jism/fulltext/v15n4/jismv15n4_2.pdf The paper explores the development of an English teaching resource library enhanced by a personalized information recommendation model based on the collaborative filtering algorithm. It highlights current challenges in China's higher education context, such as low adoption rates of digital English resources, insufficient standardization, poor inter institutional communication, and static, disorganized content that fails to meet learner's diverse needs. The author argues that integrating big data and intelligent algorithms can transform traditional, static resource libraries into dynamic, interactive platforms. Collaborative filtering is proposed as a solution to overcome issues like data sparsity and imprecise recommendations by analyzing user behavior such as click frequency and browsing history to predict preferences and deliver tailored English learning materials. The paper outlines a two layer recommendation system structure: a user interaction layer and a recommendation response layer. Experimental results from a sample of 2,000 university students show that those using the recommendation enhanced resource library demonstrated measurable improvements in English proficiency, whereas the control group did not. The study concludes that the collaborative filtering algorithm significantly boosts recommendation accuracy and learning outcomes, supporting more efficient, personalized, and engaging English education in digital environments. This approach not only optimizes resource utilization but also aligns teaching content with individual learner needs, marking a meaningful advancement in intelligent educational platforms.