Digital Trade Related Policy Text Classification and Quantification Based on TF-IDF Keyword Algorithm

With the rapid development of industrial intelligence and the increasing importance of the digital economy, the strategic position of digital trade has been significantly elevated. In order to solve the problem of low efficiency in manually interpreting policy texts, the study implemented classification and quantitative analysis for policy texts related to digital trade. And use Term Frequency Inverse Document Frequency (TF-IDF) keyword algorithm for data mining and information retrieval of text information. The results showed that the highest accuracy, recall, and F1 values achieved in text classification were 95.9%, 91.3%, and 93.4%, respectively. In summary, it can be seen that the research on the classification and quantification of policy texts related to digital trade based on TF-IDF keyword algorithm not only effectively performs intelligent text processing, but also demonstrates high accuracy. Provide support and reference for the formulation and research of policy texts related to digital trade.