摘要
本文利用人工智能融合技术优化多维数据分析,包括数据预处理、特征选择和模型构建,以提升效率和准确性。研究选取公开数据集Kaggle上的“Rossmann Store Sales”的销售数据,通过预处理、特征选择等步骤,构建了综合性特征集;设计了集成多种机器学习模型的综合算法(MDFA),整合了多源数据并引入了自然语言处理技术。实验表明,特征组合丰富时,模型预测准确性提高;MDFA在测试集上表现最优,均方误差、均方根误差和平均绝对误差均最低。本文提出的人工智能融合技术能有效提升多维数据分析的效率和准确性,为企业销售预测等任务提供技术支持。
This paper utilizes artificial intelligence fusion technology to optimize multidimensional data analysis,including data preprocessing,feature selection,and model construction,in order to improve efficiency and accuracy.The study selected sales data from the publicly available dataset"Rossmann Store Sales"on Kaggle,and constructed a comprehensive feature set through preprocessing,feature selection,and other steps;Designed a Multi-Dimensional Fusion Algorithm(MDFA)that integrates multiple machine learning models,incorporates multi-source data,and introduces natural language processing technology.Experiments have shown that when feature combinations are abundant,the accuracy of model predictions improves;MDFA performs the best on the test set,with the lowest mean square error,root mean square error,and mean absolute error.The artificial intelligence fusion technology proposed in this paper can effectively improve the efficiency and accuracy of multidimensional data analysis,providing technical support for tasks such as sales forecasting for enterprises.
作者
陶胜
TAO Sheng(School of Information Engineering,Minnan University of Technology,Shishi 362700,China)
出处
《科技创新与生产力》
2025年第8期97-101,共5页
Sci-tech Innovation and Productivity
基金
福建省中青年教育科研项目(科技类)(JAT231147)。
关键词
人工智能
多维数据
融合
自然语言处理
artificial intelligence
multidimensional data
fusion
natural language processing
