Research on the development of teaching resource library for art design majors based on artificial intelligence technology

Abstract

Abstract This paper establishes the general architecture of the teaching resource repository through the design of course clusters and specific course resource organizations for the teaching characteristics of art and design majors and builds the business process from two aspects: teacher course resource archiving and student assignment management. For the association of knowledge points of teaching resources in the repository, the coding of text semantics is completed based on BERT, the text data is enhanced by a two-way maximum matching algorithm, and the enhanced data is input into the LSTM-CRF model for training to achieve entity prediction of knowledge points. For the recognition of art design teaching resources, the F1 score of this paper’s method is improved by 9.56% compared with the ATT-CNN model and 2.65% compared with the currently known better model ATT-BLSTM, which can better characterize the semantic information of the text.