Abstract
China's Grand Canal, as an ancient artificial canal with the largest engineering scale and the longest mileage in the world, faces problems such as inconsistent terminology translation, a lack of cultural context, and low efficiency of artificial generation in its international cultural dissemination. In view of the above problems, this study proposes an intelligent generation model for the international discourse of Grand Canal culture based on Backpropagation Neural Network (BPNN). This study has built a special Chinese-English parallel corpus for the Grand Canal culture. The original data contains about 150,000 candidate sentence pairs. After strict cleaning, filtering, and manual verification, 120,000 high-quality parallel sentence pairs are finally obtained for model training and evaluation. On this basis, a three-layer feedforward network architecture is designed; momentum optimization and adaptive learning rate decay strategies are introduced. The proposed model is realized by software simulation. Based on the PyTorch deep learning framework, all experiments are completed on a NVIDIA Tesla V100 graphics processing unit. The model evaluation adopts three automatic indicators (namely Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), and Translation Edit Rate (TER)), and manual assessment. The experimental results show that the proposed model has a BLEU-4 score of 0.438, a ROUGE-L score of 0.592, and a TER of 0.385. In the manual assessment, this model's cultural fidelity score is 4.1 (out of 5). The above results have improved by 54.2% and 21.1% compared with Phrase-Based Statistical Machine Translation (PBSMT) on BLEU-4 and Google Translate on ROUGE-L. The research results verify the applicability of the optimized BPNN in the vertical cultural field; concurrently, it provides an explorable technical path for improving the accuracy and efficiency of the international communication of Chinese cultural heritage.