Intelligent Retrieval and Application of Multimedia Image Resources in Online English Language Teaching

This article aims to solve the problem of multimedia image resource retrieval in online English language teaching (ELT). By designing and implementing an intelligent retrieval algorithm model based on DL (Deep learning), this article aims to improve the accuracy and efficiency of retrieval and meet the needs of teachers and students for high-quality teaching images. In terms of methods, this article first constructs a multimedia image data set specifically for ELT, covering a wide range of instructional contents. Subsequently, CNN (Convolutional neural network) is used for feature extraction, and the educational semantics of images are deeply understood by combining with natural language processing technology. The performance of the algorithm is further improved by model optimization strategy. In the experimental design, this article adopts the method of cross-validation to ensure the fairness and accuracy of the experimental results. The results show that the proposed intelligent retrieval algorithm model has achieved remarkable performance improvement in many assessment indexes such as accuracy, recall and F1 score. Futhermore, the user satisfaction survey shows that teachers and students are generally satisfied with the retrieval results, and think that the high matching between images and instructional content is helpful to improve teaching effect and learning experience.