The analysis of bidirectional long short-term memory network model for construction of cultural gene map and information extraction

To create a cultural gene map and extract information, this paper introduces a two-way long and short-term memory network (LSTM) model and verifies it using Jinxiu Yao headwear as an example. In this paper, a thorough cultural gene map, comprising structure, pattern, and color maps, is successfully constructed using the Bidirectional Long Short-Term Memory Network (Bi-LSTM) model. Experimental results show that the proposed model exhibits excellent performance in the information extraction task. On the test set, its Accuracy is 0.94, Precision is 0.92, Recall is 0.91, F1-score is 0.92, and AUC is 0.91. Compared with traditional models including LSTM, Gated Recurrent Unit Network (GRU), Convolutional Neural Network + Long Short-Term Memory Network (CNN-LSTM), Transformer, and Bidirectional Encoder Representations from Transformers + Bidirectional Long Short-Term Memory Network (BERT-BiLSTM), the proposed model performs significantly better. The model demonstrates outstanding performance in processing complex sequence data. Meanwhile, it can efficiently capture cross-modal and multi-dimensional cultural information, providing strong data support for the digital research and protection of traditional cultural works. This shows that the cultural gene information extraction method has broad application prospects. In the future, the model performance can be further optimized and the application scenarios can be expanded.