事故隐患分类能够直观反映企业安全生产管理的薄弱点，同时将直接决定企业优化安全管理工作的方向。油田安全生产过程中，隐患种类多，数据量大，单纯依赖人工方式分类及管理效率较低，且难以发掘数据中蕴含的潜在规律。基于油田安全生产的需求及事故隐患特征，提出了一种基于BERT-BiLSTM的分类模型，用于油田安全生产隐患文本的主题自动分类，通过基于Transformer的双向编码器表示（bidirectionalencoder representations from Transformer, BERT）模型提取输入文本的字符级特征，生成全局文本信息的向量表示，再通过双向长短时记忆网络（bi-directional long short-term memory, BiLSTM）模型对局部关键信息和上下文深层次特征进行特征提取，进而通过Softmax激活函数进行概率计算得到分类结果。通过与传统分类方法的比较表明，BERT-BiLSTM分类模型在加权平均准确率、加权平均召回率和加权平均F₁等指标方面均有所改善，模型与油田企业现有安全管理信息系统的有机融合将为进一步提升油田企业的事故隐患管理针对性，推动企业安全管理从事后被动反应向事前主动预防转变提供重要的技术支撑。

The weaknesses in enterprise safety production management are reflected in accident hazard classification, which determines the direction for optimizing safety management. In the process of oilfield safety production, a large amount of data and various types of hazards make it inefficient to rely solely on manual classification and management, as well as difficult to uncover potential patterns hidden in the data. Based on the requirements of oilfield safety production and the characteristics of hidden danger, a classification model based on BERT-BiLSTM was proposed for automatic topic classification of hidden danger text in oilfield safety production. Through the bidirectionalencoder representations from Transformer (BERT) model, character-level features of input text were extracted and vector representations of global text information were generated. Then, the bi-directional long short term memory (BiLSTM) model was used to extract local key information and contextual deep features, and the classification results were obtained by probability calculation using Softmax activation function. Comparisons with traditional classification methods reveal that the BERT-BiLSTM classification model has shown improvements in weighted average accuracy, weighted average recall, and weighted average F₁-score. Integrating the model with the existing safety management information system of oilfield enterprises will enhance targeted management of accident hazards and provide important technological support to shift enterprise safety management from reactive response to proactive prevention.