A Model Based on Fuzzy Neural Networks for Sharing Digital Educational Resources in English

The ineffective digitization of core English course resources, due to limited autonomy, fragmentation, and inadequate management, has prompted the development of digital teacher libraries using multimedia and Internet technologies. Fuzzy neural networks (FNNs), combining fuzzy and neural network controls, have emerged as a promising approach for mathematical modeling. This paper presents an FNN-based model for sharing digital English educational resources and proposes an effective guidance mechanism for sharing digital science education resources. Experimental results show that the FNN outperforms the Apriori algorithm in training sample error by 0.02069 and reduces running time by 0.0034 seconds on average. This indicates the FNN's superior approximation capabilities with appropriate initial fuzzy rules. Consequently, the FNN-based model enhances the value and quality of educational resources, expands the capacity of information resource libraries, and effectively mitigates the creation of low-quality resources.