Integration of Deep Machine Learning for Digital Economy Resource Allocation Optimization Model

DOI

10.2991/978-94-6463-992-6_44How to use a DOI?

Keywords

Digital Economy; Deep Machine Learning; Resource Allocation Optimization; Deep Reinforcement Learning; Convolutional Neural Network

Abstract

In the digital economy context, traditional resource allocation methods struggle with massive data and complex decisions. This paper constructs a multi-level optimization framework integrating CNN, LSTM, and deep reinforcement learning for intelligent resource allocation. Based on Google Cluster Trace dataset, the CNN-LSTM-QMIX model achieves 78.3% resource utilization (5.2% improvement), 5.3% demand forecasting MAPE, and maintains 74.6% utilization under ± 10% error, proving good robustness. Cross-scenario analysis reveals transfer potential in e-commerce supply chains and cross-border logistics, providing technical support for digital economy resource optimization.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tianbo Xing
AU  - Bin Liu
AU  - Zitong Zheng
AU  - Sheng Zhang
PY  - 2026
DA  - 2026/02/20
TI  - Integration of Deep Machine Learning for Digital Economy Resource Allocation Optimization Model
BT  - Proceedings of the 2025 4th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2025)
PB  - Atlantis Press
SP  - 472
EP  - 481
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-992-6_44
DO  - 10.2991/978-94-6463-992-6_44
ID  - Xing2026
ER  -