Cyber Threat Intelligence (CTI) helps organizations understand the tactics, techniques, and procedures used by potential cyber criminals to defend against cyber threats. To protect the core systems and services of organizations, security analysts must analyze information about threats and vulnerabilities. However, analyzing large amounts of data requires significant time and effort. To streamline this process, we propose an enhanced architecture, BERT-CRF, by removing the BiLSTM layer from the conventional BERT-BiLSTM-CRF model. This model leverages the strengths of deep learning-based language models to extract critical threat intelligence and novel information from threats effectively. In our BERT-CRF model, the token embeddings generated by BERT are directly fed into the Conditional Random Field (CRF) layer for efficient Named Entity Recognition (NER), thus preventing the need for an intermediate BiLSTM layer. We train and evaluate the model with three publicly available threat entity databases. We also collect open-source threat intelligence data from recent years for evaluating the applicability of the constructed model in a real-world environment. Furthermore, we compare our model with the most popular GPT-3.5 and the most downloaded open-source BERT question-and-answer models. Through this study, our proposed model demonstrated robust usability and outperformed other models, signifying its potential for application in CTI. In a real-world scenario, our model achieved an accuracy of 82.64%, while with malware-specific threat intelligence data, it achieved an impressive accuracy of 93.95%. The code for this research is publicly available at https://github.com/stwater20/ner_bert_crf_open_version.