Journal of Engineering, Project, and Production Management, 2026, 16(2), 2025-180
An Archive Resource Management System Based on Multi-Head Self-Attention Mechanism and LSTM
Lecturer, School of Literature and Journalism & Communication, Yan’an University; Yan’an, 716000, China, E-mail: cuihuatougao@163.com
Production Management
Received August 24, 2025; revised October 11, 2025; accepted October 14, 2025
Available online March 7, 2026
Abstract: Archival resource management, as a critical foundation of the information society, faces challenges in efficient storage, accurate retrieval, and intelligent analysis of multimodal data. Traditional approaches relying on manual classification and keyword-based retrieval struggle with semantic understanding, predictive modeling, and real-time anomaly detection. To address these issues, this study proposes a novel archival resource management system that integrates Multi-Head Self-Attention (MHSA), Temporal Convolutional Network (TCN), and Long Short-Term Memory (LSTM). The novelty of this system lies in the combination of a global semantic representation of MHSA, multi-scale time-series modeling with TCN, and long-term dependency capture with LSTM, further enhanced by a phase mechanism and Gated Linear Unit (GLU) to optimize feature selection. Experimental evaluations on CMU and MIMIC-III datasets demonstrate that the system achieved superior performance in multiple tasks: feature similarity of 0.97, clustering accuracy of 98.25%, classification accuracy of 96.42%, F1-score of 95.28%, time-series forecasting RMSE of 0.31, MAE of 0.23, anomaly detection accuracy of 94.37%, and a low false alarm rate of 2.81%. Moreover, the system maintains robustness under noisy conditions and generalizes well across different archival datasets. These results highlight the effectiveness and originality of the proposed framework, providing a feasible solution for building intelligent archival resource management systems with high precision, predictive capability, and anomaly monitoring.
Keywords: Archival data, resource management, attention mechanism, LSTM, multi-modal data
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Citation: Cui, H. (2026). An Archive Resource Management System Based on Multi-Head Self-Attention Mechanism and LSTM. Journal of Engineering, Project, and Production Management, 16(2), 2025-180.
