Construction of Chinese Intangible Cultural Heritage Knowledge Graph Based on Deep Learning

A deep learning based named entity recognition method is proposed. Although BiLSTM has a good effect in named entity recognition task, it cannot focus on the local context near the target word in both forward and backward propagation. To address this issue, we propose the utilization of a convolutional neural network, which limits the receptive field through the volume base and encodes the character twice, so that the machine pays more attention to detail information when understanding the text, compared with the BiLSTM method, the F1 value of the proposed model is increased from 0.875 to 0.902, an increase of 0.027. A text classification method based on large pre-training model was proposed for text cleaning, compared with BERTBiLSTM, the F1 value of the proposed model in the Chinese nonposthumous text classification task is increased from 0.85 to 0.87, an increase of 0.02. After extracting information through named entity recognition method, the Chinese intangible cultural heritage knowledge graph is constructed.