【目的/意義】引入人工智能領域中的深度學習方法來解決數字圖書館中傳統文本分類的缺陷,這既是人工智能領域研究的重點,也是圖書館領域關注的熱點問題。【方法/過程】在對數字圖書館傳統文本分類進行系統梳理的基礎上,提出基于深度學習的數字圖書館文本分類模型,利用詞向量的方法對文本特征進行表示,采用深度學習模型中的卷積神經網絡提取文本信息的本質特征,并進行了實驗驗證。【結果/結論】實驗測試表明,基于深度學習的文本分類模型可以有效地提高數字圖書館文本分類的準確率和召回率,不僅可以提高數字圖書館內部業務的智能化程度,還可以提高數字圖書館信息服務的效率和質量。

【Purpose/significance】 This paper introduces deep learning methods in the field of artificial intelligence to solve the defects of traditional text classification in digital libraries. This is not only the focus of research in the field of artificial intelligence, but also a hot issue in the library field. 【Method/process】 On the basis of the systematic summarization of the traditional text classification in digital libraries, this paper puts forward a digital library text classification model based on deep learning, it uses the word embedding method to represent text features, and it uses the convolutional neural network in the deep learning model to extract the essential features of the text and it has been experimentally verified it.【Result/conclusion】Experimental tests show that the text classification model based on deep learning can effectively improve the accuracy and recall of text classification in digital libraries. It can not only improve the intelligence of the digital library internal business, but also improve the efficiency and quality of digital library information services.