随着能源行业的快速发展和技术革新，大量的专业术语和表达方式不断更新，新词不断涌现。然而，传统的新词发现方法通常依赖于词典或规则，且难以高效率地处理和更新大量的专业术语，特别是在快速变化的能源领域。因此，结合能源领域文本数据特性，提出了一种融合N-Gram和多重注意力机制的能源领域新词发现方法（new word discovery method in the energy field combining N-Gram and multiple attention mechanism, ENFM）。该方法首先利用N-Gram模型对能源领域的文本数据进行初步处理，通过统计和分析词频来生成新词候选列表。随后，引入融合多重注意力机制的ERNIE-BiLSTM-CRF模型，以进一步提升新词发现的准确性和效率。与传统的新词发现技术相比，在新词的准确识别和整体效率上均有显著提升，将其于能源领域政策文本数据集，准确率、召回率和F₁分别为95.71%、95.56%、95.63%。实验结果表明，该方法能够准确地在能源领域的大量文本数据中识别新词，有效识别出能源领域特有的词汇和表达方式，显著提高了中文分词任务中对能源领域专业术语的识别能力。

With the rapid development of the energy industry and technological innovation, a large number of professional terms and expressions are constantly updated, and new words continue to emerge. However, traditional neologism discovery methods often rely on dictionaries or rules, and it is difficult to efficiently process and update a large number of specialized terms, especially in the rapidly changing energy field. Therefore, combined with the characteristics of text data in the energy field, a new word discovery method in ENFM(energy field combining N-Gram and multiple attention mechanism) was proposed. Firstly, the N-Gram model was used to process the text data in the field of energy, and the candidate list of new words was generated by statistics and analysis of word frequency. Subsequently, the ERNIE-BiLSTM-CRF model integrating multiple attention mechanism was introduced to further improve the accuracy and efficiency of neologism discovery. Compared with the traditional neologism discovery technology, the accurate identification and overall efficiency of neologism have been significantly improved. The accuracy rate, recall rate and F₁ value of neologism in the data set of policy text in the energy field are 95.71%, 95.56% and 95.63%, respectively. The experimental results show that this method can accurately identify new words in a large number of text data in the field of energy, effectively identify the specific words and expressions in the field of energy, and significantly improve the recognition ability of professional terms in the field of energy in Chinese word segmentation tasks.