Abstract
Abstract As a critical component of information management, the development of digital and information archive systems enhances both the efficiency and quality of enterprise operations, thereby increasing economic value. This paper employs the ReLU activation function to augment the non-linear capabilities of the neural network model. Additionally, we introduce the MP-CNN model, designed to extract and recognize textual content from archival images. These images are initially processed using the BP neural network algorithm, followed by grayscale conversion for clarity, and subsequently backed up for analytical purposes. The implementation of intelligent archive management resulted in a more than 35% improvement in efficiency. The distribution interval of the Return on Investment (ROI) values from archival data at the test site ranged between [0,5], displaying a concentrated pattern that aligns with the typical economic value distribution. The integration of AI technology in archive management not only streamlines processes but also maximizes resource utilization and enhances returns.
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