Intelligent Classification Algorithm for Online Teaching Resources in Higher Vocational Colleges Integrating Multimodal Information

Inside the realm of higher vocational training, the clever magnificence of on-line coaching property is pivotal for reinforcing educational performance and accessibility.This paper introduces a novel approach that amalgamates the Transformer model's functionality in processing and integrating multimodal statistics with the computational efficiency of the shufflenet version.The proposed approach to begin with employs the Transformer to adeptly take care of numerous modalities inside teaching assets, encompassing textual content, pix, motion snap shots, and audio.Subsequently, recognizing the computational constraints time-venerated in lots of vocational establishments, the shufflenet model is deployed.This model makes use of pointwise organization convolution, channel shuffling, depth-practical separable convolution, and light-weight interest modules, setting a balance amongst pace and accuracy in classifying academic content material material.The experimental results screen a full-size development over traditional methods in the course of a couple of metrics, organising a brand new benchmark in the sort of online training sources in better vocational training.