Intelligent Recommendation of Electronic Certificate Based on Naive Bayes Model
Digital government is a crucial component in the establishment of an efficient digital governance system,and the investigation into intelligent electronic licensing services contributes to the reform efforts of digital government.By integrating the naive Bayes model with governmental data from a specific province,the effectiveness of the model is validated.The findings reveal that the top 10 frequently used office types by legal entities account for 49.46%of total usage,significantly surpassing individual-based government service types.The recommendation accuracy for individual and legal entity certificates in this province reaches 53.22%and 68.51%,respectively,with even higher accuracy rates achieved when constructing models based on specific cities,reaching up to 96.24%in certain regions.Reducing the time window for determining priority relationships among government service events leads to a significant decrease in recommendation accuracy.These results demonstrate both the efficacy of the methodology which can provide valuable insights that can support and inform efforts aimed at enhancing governmental efficiency.
digital governmentNaive Bayes modelelectronic licensegovernment service events