Aiming at the lack of named entity corpus in the satellite domain and the low recognition performance of existing algorithms, a satellite domain entity labeling method considering fuzzy boundaries was proposed, constructed a corpus containing 8 common satellite domain entities where the granularity was finer and the coverage was wider in comparison with the existing corpora in this field. Based on this, a transfer learning and multi-network fusion satellite domain entity recognition algorithm was proposed. Algorithm used pretrained bidirectional encoder representations for transformers to smoothly transfer the semantics of the corpus for subword-level features, a BiLSTM (bi-directional long-short term memory) network for capturing contextual information to determine boundaries, and label prediction was achieved using a conditional random field as a decoder. Experimental results show that, compared with traditional models such as BiLSTM, the proposed algorithm has better recognition performance where the F₁-score in 8 entities is all above 92% and the micro-average F₁-score reaches 96.10%.