A Deep Learning-Based Adaptive Classification Method for Educational Materials for Ideological and Political Instruction Within Higher Education Institutions

In colleges and universities, resources for ideological and political education are available in various forms, increasing the complexity and difficulty of resource classification. To address this challenge, we proposed an adaptive classification method based on deep learning. We used DSNet-based key frame summary model to process ideological and political education video resources, obtained key frame sequences and input them into the image feature extraction network with ResNet34 residual network and SE module to generate the video resource feature map. We input college ideological and political education text resources into the Word2Vec-based word vector model, convert words into word vectors, use TF-IDF and CTF-IDF to obtain the vector representation of text resources and input them into the ABLCNN-based text feature extraction network to generate the feature map of text resources. We use a multimodal fusion network to integrate the characteristics of textual and video materials for the classification of ideological and political educational content. The empirical findings show that the F1-Score metric peaks when 32 key frames are used and this approach effectively classifies ideological and political educational resources in higher education with a comprehensive classification precision of 98.87%.