This study developed an innovative method for allocating English multimedia network teaching resources to overcome limitations in traditional approaches, including poor validity, low matching accuracy, and process inefficiency. The proposed solution involved three key phases. First, teaching resources were collected and processed using Kalman filtering for noise reduction; subsequently, comprehensive feature extraction was performed through multi-task width learning; finally, user preferences were calculated via an interest model while resource matching was optimised using graph planning algorithms. The allocation model incorporated Pearson correlation coefficients to enhance precision. Experimental results demonstrated exceptional performance metrics: allocation validity approaching 100%, absolute matching error consistently below 0.2%, and allocation efficiency reaching 99%. These outcomes confirmed the method's significant improvement over conventional techniques, particularly in digital education reform. The system's robust performance stemmed from its integrated approach combining advanced filtering, comprehensive feature analysis, and intelligent matching algorithms, establishing a new performance benchmark for educational resource allocation systems in both accuracy and operational efficiency.
Yang et al. (Thu,) studied this question.