基于朴素贝叶斯模型的电子证照用证智能推荐

数字政务是数字政府建设中的重要组成部分，研究电子证照智能服务有助于数字政务改革。结合朴素贝叶斯模型与某省政务数据构建用证预测模型，并对模型的有效性进行检验。结果表明：法人类型高频办件前10种组合占全部组合的49.46%,远高于个人类型政务服务事件；全省范围内个人用证、法人用证推荐准确率分别可达到53.22%、68.51%,按地市构建模型能够有效提升准确率，部分区域准确率最高可达96.24%;缩小判定政务服务事件先后关系时间窗口，推荐准确率明显降低。研究结果表明了研究方法的有效性，有关方法和结论对提升政府效能具有支撑参考作用。

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 thatthe 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.