A Personalized Course Resource Recommendation Method Based on Deep Learning in an Online Multi-Modal Multimedia Education Cloud Platform

Aiming at the problem that unstructured text in online multi-modal multimedia education is easy to cause error propagation, this paper proposes a personalized course resource recommendation method using deep learning in online multi-modal multimedia education cloud platform. First, the word vector of the text is obtained from the course data set by using the BERT pre-training model, and its semantic information in different contexts is analyzed. Then, the more complex representation of each word is extracted through the long short-term memory network (LSTM), in which the multi-head attention layer adds different weights to different word vector to better capture the key information in the sentence. Finally, the CRF layer is used to identify sentence entities, and the Sigmoid layer is used to extract relations, thus completing personalized course resource recommendation, which is significantly improved compared with other models. Experimental analysis shows that the algorithm is effective in personalized course resource recommendation.