Design of Marketing Digital Control System Based on the Integration of Big Data Analysis and Machine Learning

The advent of the digital age has made it difficult for traditional marketing methods to meet the rapidly changing needs of the market. To improve the efficiency and effectiveness of enterprise marketing activities, it is particularly important to develop intelligent and precise marketing management systems. Therefore, the study proposes a marketing digital control system based on the integration of big data analysis and machine learning, which utilizes the Apache Flink distributed big data processing framework to design a marketing control system that includes system content and functional requirements. At the same time, machine learning design ideas are introduced into marketing recommendation algorithms, using reinforcement learning to enrich the business logic of the Rete network, dynamically generate and update rules, and calculate user interest while ensuring the fit between marketing recommendation content and user interest information. The study constructed a self-designed dataset (consisting of over 30000 pieces of data) through data simulation and crawling, and compared the research method with other machine learning algorithms on the same dataset. The results show that the maximum matching accuracy of the improved recommendation algorithm reaches 90.12%, and the prediction accuracy of user consumption behavior exceeds 88%, which is better than other comparative algorithms. The mean absolute error value on the product is less than 0.10, and the F1 value is greater than 0.65, indicating significant recommendation effectiveness. The research-designed marketing digital control system effectively integrates big data analysis and machine learning technology, providing support for the digital transformation of enterprises and the intelligent upgrading of related fields.

@article{Li2025,
  title     = {Design of Marketing Digital Control System Based on the Integration of Big Data Analysis and Machine Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Qiming Li and Songling Du and Shiyuan Zhang},
  doi       = {10.14569/IJACSA.2025.0160812},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160812}
}