The knowledge regarding the structure-activity relationships of nickel-based single crystal superalloys is mainly stored in the form of unstructured text in the vast published scientific literature, and its effective utilization can accelerate the design of high-performance materials. Named entity recognition (NER) methods can be used to extract vital information from unstructured text, thus contributing to automatically achieving tedious text mining tasks. However, existing NER methods typically rely on a large amount of corpus data, especially of the deep-learning-based type, and can hardly tackle cross-domain tasks. To the best of our knowledge, no prior research has been conducted for the knowledge discovery of nickel-based single crystal superalloys based on deep-learning-based NER; thus, it is difficult to adapt existing methods to this field. Here, a semantic-features-fused NER (SF-NER) method based on deep learning was proposed, aiming to accurately extract knowledge from abstract text concerning nickel-based single crystal superalloys. Specifically, as data quality determines the performance of NER models, a high-quality annotated corpus dataset for the above-mentioned superalloys (containing 19405 entity data of eight entity types) was constructed. This was created via remote supervision using domain-specific materials dictionary under the domain knowledge's guidance. To accurately capture the terms related to specific materials from the high-quality corpus dataset, a encoding fusion strategy for word representation was proposed for encoding the essential semantic features of materials from various perspectives. Then, based on these semantic features, a deep learning model, i.e., bidirectional long short-term memory-cenditional random field (Bi-LSTM-CRF), was built to capture key semantic information in sentence sequences, thus accurately predicting entity types. The results of the experiment demonstrated that the proposed SF-NER method could accurately distinguish the entity categories of nickel-based single crystal superalloys (i.e., F1 = 0.84) and effectively identify the key factors influencing their service performance. Lastly, descriptors with high importance were recommended, as they can be employed for machine learning modeling to explore the structure-activity relationships of high-performance materials.