School of Business and Economics, University Putra Malaysia, 43400 Seri Kembangan, Malaysia.
Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
Comput Intell Neurosci. 2022 Apr 11;2022:1499801. doi: 10.1155/2022/1499801. eCollection 2022.
The rapid development of artificial intelligence technology has led to rapid development in various fields. It has many hidden related customer behavior information and future development trends in the e-commerce information system. The data mining technology can dig out useful information and promote the development of e-commerce. This research analyzes the significance and advantages of data mining technology in the application of e-commerce management systems and analyzes the related technologies of data mining and future trend prediction. This research has taken the advantages of clustering and naive Bayesian methods in data mining to classify product information and purchase preferences and other information and mine the associated data. Then, the nonlinear data processing advantages of neural networks are used to predict future purchasing power. The results show that data mining technology and neural networks have high accuracy in predicting future consumer purchasing power information. The correlation coefficient between real consumption data and predicted consumption data reached 0.9785, and the maximum relative average error was only 2.32%. It fully shows that data mining technology can obtain some unrecognizable related information and future consumption trends in e-commerce systems, and neural networks can also predict future consumption power and consumption patterns well.
人工智能技术的飞速发展，使得各个领域也得到了飞速发展。电子商务信息系统中蕴藏着许多潜在的相关客户行为信息和未来发展趋势。数据挖掘技术可以挖掘出有用的信息，从而促进电子商务的发展。本研究分析了数据挖掘技术在电子商务管理系统中的应用意义和优势，并对数据挖掘的相关技术和未来趋势预测进行了分析。本研究采用数据挖掘中的聚类和朴素贝叶斯方法的优势，对产品信息和购买偏好等信息进行分类和挖掘相关数据。然后，利用神经网络的非线性数据处理优势，预测未来的购买能力。研究结果表明，数据挖掘技术和神经网络在预测未来消费者购买能力信息方面具有较高的准确性。实际消费数据与预测消费数据之间的相关系数达到 0.9785，最大相对平均误差仅为 2.32%。这充分表明，数据挖掘技术可以在电子商务系统中获取一些难以识别的相关信息和未来消费趋势，神经网络也可以很好地预测未来的消费能力和消费模式。
