Applying Convolutional Neural Networks on Automatic Classification of Digital Learning Content

In the backdrop of the swift progression of information technology, digital learning has been extensively applied within the educational domain. There has been an explosion in the quantity of digital learning resources, yet this vast number of resources has concurrently led to severe issues regarding management and utilization. Figuring out how to promptly and precisely locate content that aligns with requirements from a vast array of digital learning resources has emerged as the linchpin for enhancing learning efficiency and quality. In response to the challenges of low efficiency and inaccuracy in the management and categorization of digital learning resources, the convolutional neural network (CNN) technology is introduced to heighten the automation and precision of classification. Based on this, this paper meticulously analyzes the operational principle of the CNN and constructs an efficient digital learning content classification model founded on it. This model is capable of fully capitalizing on the advantages of CNN in feature extraction and pattern recognition to accurately categorize digital learning resources. To validate the performance of the model, this paper undertakes a systematic experimental validation. The experimental outcomes indicate that the classification model based on CNNs outperforms the traditional machine learning algorithm in terms of the accuracy of digital learning content classification tasks, with its accuracy rate ultimately stabilizing at 0.94. In practical applications, in comparison with traditional approaches, the model has remarkably enhanced classification accuracy, effectively resolved the problem of digital learning resource classification, and furnished a more intelligent and efficient classification solution for the field of educational technology.