In recent years, research on the named entity recognition of poetry in digital humanities is emerging, but few studies have been conducted with regard to the feature expressiveness of character features, word segmentation accuracy, and the effectiveness of domain-specific knowledge in poetry texts. According to the characteristics of Chinese pictographs and the particularity of poetry texts, a recognition method of named poetry entities with a feature enhancement unit and a feature extraction unit is proposed, which integrates multiple features such as characters, radicals, sounds, and metrical rules. The method presents the knowledge vectors obtained from the knowledge triples of tune pattern titles through the ANALOGY model as the knowledge vectors of tune pattern titles. Then, the radical vector, character vector, metrical rule vector, sound vector, and knowledge vector of tune pattern titles are deeply fused through the bidirectional long short-term memory network and attention mechanism models. In this way, the recognition method of named poetry entities fusing multi-features is constructed. The results of comparative experiments and ablation experiments on the self-made corpus of Translation of Among Flowers (Hua Jian Ji) (《花间集全译》) show that the proposed method can effectively use multi-features to improve the recognition performance of named entities, and its F1 score reaches 85.63%, which means it completes the recognition task of named poetry entities.