Research on Online English Teaching Resource Allocation Strategy Based on Optimization Algorithm

With the arrival of the era of education informatization, online learning has become one of the research hotspots. Aiming at one of the important links, learning resource allocation, this paper designs an online system for distributing English teaching resources. Based on the maximum distance optimization K-means clustering algorithm, a hybrid collaborative filtering personalized recommendation algorithm based on users and items is used to optimize the similarity algorithm, and the online English teaching resources allocation strategy is proposed. A variety of teaching resource recommendation quality experiments are set up and combined with teaching practice applications to explore the model’s resource allocation quality and its enhancement effect on English teaching. The model in this paper has higher recommendation accuracy and recommendation quality of English teaching resources, and the comprehensive evaluation index value F is lower than that of the comparison model by 0.18 and 0.07. At the same time, the online English teaching resources allocation strategy has a better effect on improving learning attitudes, with more than 72% of positive evaluations in each dimension of satisfaction. The experiment shows that the online English teaching resource allocation strategy has a positive effect on students’ English learning efficiency and motivation.