摘要
随着全球数字化转型的加速,公共数据作为新型生产要素的重要性日益凸显。然而,当前公共数据价值评估方法存在主观性强、适用范围有限等问题,难以满足数据市场化配置的需求。研究选取数据容量、市场关注度、数据应用度、数据新鲜度等九项特征指标,分别采用B-P神经网络和随机森林模型进行实证分析。结果表明,随机森林模型在准确率、精确率、召回率和F1得分上均显著优于B-P神经网络,其中数据访问量和下载次数对价值评估的影响最为显著。文章建议,提升数据开放质量与用户参与度,优化高需求领域数据的访问和下载机制;建立动态更新与共享机制,确保数据时效性并扩大应用场景;利用随机森林模型定期迭代训练,科学制定数据开放政策。
With the acceleration of global digital transformation,the importance of public data as a new type of production factor is increasingly prominent.However,the current methods for evaluating the value of public data have problems such as strong subjectivity and limited applicability,which makes it difficult to meet the demands of market-based allocation of data.The present research selected nine characteris tic indicators such as data capacity,market attention,data application degree,and data freshness,and conducted empirical analyses using B-P neural network and random forest model respectively.The re sults have shown that the random forest model significantly outperforms the B-P neural network in terms of accuracy,precision,recall rate,and F1 score.Among them,the impact of data access volume and download frequency on value assessment is the most significant.The article therefore suggests enhancing the quality of data openness and user engagement,optimizing the access and download mechanisms for data in high-demand areas;establishing a dynamic update and sharing mechanism to ensure data timeli ness and expand application scenarios;and using the random forest model for regular iterative training to scientifically formulate data openness policies.
作者
李毅
姚梁皓
LI Yi;YAO Liang-hao(School of Information,Shanxi University of Finance and Economics,Taiyuan Shanxi 030006;School of Applied Economics,Remin University of China,Beijing 100872,China)
出处
《太原理工大学学报(社会科学版)》
2025年第3期120-127,136,共9页
Journal of Taiyuan University of Technology(Social Science Edition)
基金
山西省统计科学研究重大项目(2024D005)
山西省基础研究计划杰青项目(202303021223010)
山西省留学人员科技活动择优资助重点项目(20220025)。
