Optimization and retrieval method of standard address library data under information segmentation analysis

New place names and proper nouns in standard address database data are prone to getting trapped in high-frequency candidate databases, making it difficult to effectively obtain the word string with the highest cumulative probability, which affects the recall, precision, and retrieval time of the retrieval process. In order to optimize the retrieval performance of standard address database data, a standard address database data optimization retrieval method based on information segmentation analysis is proposed. By using the full segmentation algorithm to generate all possible word string segmentation methods, and evaluating the probability of each segmentation method using statistical language models, while introducing the concepts of left neighbor words and the best left neighbor words to reduce computational complexity, the word string with the highest cumulative probability is selected as the segmentation result. This process does not require manual establishment of a rule library, achieving efficient and accurate segmentation. Analyzing the segmented standard address library information, gradually increasing the word count from the combination of two characters, and querying whether there are corresponding words in the standard address library. Based on the writing rules and structure of the address, gradually parsing the various components of the address improves the efficiency of address information parsing and can better handle new place names and proper nouns. We compared the number of standard address search terms obtained by different parsing methods with the number of unregistered standard address search terms, and selected the optimal search term based on the comparison of word frequency data and word frequency sum. By mining the word frequency information in the standard address database search dictionary, we obtained the optimized standard address database data search results. The experimental results show that the proposed method has high NDCG during the retrieval process of standard address database data, and has excellent recall, precision, and retrieval time, optimizing the performance of standard address database data retrieval.