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As blockchain technology research continues to advance, named entity recognition within blockchain patent texts has emerged as a fundamental cornerstone for various other tasks. In light of the absence of a publicly accessible corpus within the current realm of blockchain technology, as well as the challenges posed by insufficient text semantic expression, the dearth of semantic features, and the intricacies associated with capturing diverse word vector representations. This paper us of annotated blockchain patent texts and proposes the MBBC MacBert-BiLSTM-CRF MBBC model for named entity recognition in blockchain patents. The model employs embedding the feature layer to vectorize the input text, enriching text information and mitigating polysemy issues. Text feature information is acquired through the long short-term memory network, and the conditional random field model is integrated to enforce label relationships, yielding an optimal label sequence. In this study, the accuracy rate of BAT (Blockchain Application Technology, BAT) on the self-constructed corpus achieves 91%, with a recall rate of 96% and an Fl value of 93%, representing a notable enhancement compared to mainstream models. Ablation experiments further demonstrate that the model in this paper effectively performs named entity recognition tasks associated with blockchain technology and subject content.
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