DRIIS: Research on Automatic Recognition of Artistic Conception of Classical Poems Based on Deep Learning

Poetry is the jewel in the crown of our country’s classical culture and has been praised and studied by countless people for thousands of years. In recent years, with the rapid development of computer technology and the leap-forward improvement of hardware computing power, natural language processing (NLP) technology has achieved remarkable results in practice. We applied NLP to the text analysis of classical poetry, proposed a set of methods to automatically recognize the artistic conception in classical poetry, and established the classical poetry artistic conception dataset for experimentation through the crawler method. In the experiment, we studied the application of different machine learning algorithms in text classification, combined such algorithms with different document vectorization methods, compared their performance on the topic classification problem of poetry, and concluded that there are some better accuracy rates under the classical machine learning framework. Comparing the effects of word-based vectors and word-based vectors, we concluded that the ancient poetry word vectors constructed based on characters have a higher accuracy rate. We also further introduced deep learning methods into the research, analyzed the pros and cons of various neural networks, and studied the neural network architectures that have good results in the practice of NLP, such as TextCNN and BiLSTM models. We also introduced mature NLP pre-training models such as BERT to classify the artistic conception of classical poetry. In addition, we also constructed an emotional dictionary matching method based on word vectors for sentiment analysis. The experimental results have shown that the method proposed in this paper has a good effect of automatic recognition of classical poetry mood, which can be used to recommend similar poems and select poems with emotion as the theme through the poetry mood.