The Applications and Research of NLP and Deep Learning Technologies in Public Security Data

With the deepening reform and construction of the police system, it is imperative to fully utilize technologies such as artificial intelligence, big data, blockchain, cloud computing, and the Internet of Things to rapidly enhance the intelligence level of the new generation command center. Natural language processing and deep learning technologies can achieve recognition, understanding, and correlation analysis of public security textual information, which are core steps in building intelligence. Due to the diversity of information in the public security field, the accuracy of these technologies still requires further research. In the analysis of public security incidents, the application of NLP technology faces challenges in optimizing technical models and algorithms, which greatly enhances the efficiency and accuracy of public safety management by analyzing, understanding, predicting, and responding to various security challenges in an intelligent manner. With the advent of the big data era, the public security sector has accumulated massive amounts of police incident data, which contain rich information but also bring difficulties in processing and analysis. Traditional NLP models often struggle to cope with the complex and ever-changing police incident texts, leading to compromised accuracy and reliability of the analysis results. This paper will explore the key technological applications of NLP and deep learning in the field of public safety and provide a review of their research progress.