基于机器学习算法的信访风险预测模型研究  

Research on Petition Risk Prediction Model Based on Machine Learning Algorithm

作　　者：蔡韧 CAI Ren(Hebei Province Letters and Visit Information Analysis and Research Center,Shijiazhuang 050051,China)

机构地区：[1]河北省信访信息分析研究中心,石家庄050051

出　　处：《移动信息》2025年第4期252-253,271,共3页Mobile Information

摘　　要：当前,信访风险管理面临精准预测难、预防机制薄弱等问题,亟需改善。为解决该问题,文中提出了一种基于机器学习算法的信访风险预测模型。该模型通过对历史信访数据的分析,从中提取关键特征,并利用支持向量机算法对风险事件进行建模和预测。在模型训练过程中,采用超参数调优来提升预测精度。实验结果表明,该模型在信访风险预测中具有较高的准确性和鲁棒性,能有效地识别潜在的风险事件,实现对信访风险的精准预警。At present,petition risk management is facing problems such as difficulty in accurate prediction and weak prevention mechanism,which need to be improved urgently.To solve this problem,this paper proposes a petition risk prediction model based on machine learning algorithm.The model extracts key features through the analysis of historical petition data,and uses support vector machine algorithm to model and predict risk events.In the process of model training,hyperparameter tuning is used to improve the prediction accuracy.Experimental results show that the model has high accuracy and robustness in petition risk prediction,and can effectively identify potential risk events and achieve accurate early warning of petition risk.

关 键 词：机器学习算法 SUM 信访风险预测 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论] C939[自动化与计算机技术—计算机科学与技术]