Abstract: With the widespread application of big data technology in the field of public security,the improvement of police response speed has become one of the core goals to promote the modernization and efficient operation of public security.The rapid response system for police incidents replaces traditional manual dispatch with an automatic dispatch mechanism,and its core relies on the model’s accurate identification of police addresses.However,there are significant differences in feature representation between police addresses and regular addresses,and existing commercial address matching models often suffer from insufficient adaptability when dealing with police addresses.To address this issue,this paper proposes an improved method that combines address grading and pinyin information,aiming to replace traditional deep learning algorithms and address the limitations of commercial address calculation models in police address recognition.This method is optimized for the special phrases,multi-level address structure,homophones,and misspellings in Chinese police addresses.By using techniques such as pre-training models,data augmentation,address grading,and Pinyin information encoding,this paper aims to develop and train an efficient model specifically designed for calculating the similarity of police addresses,significantly improving the recognition accuracy and adaptability of Chinese police addresses.

Key words: Police address, Address classification, Pinyin, Deep learning, Pre-training, Data augmentation