Research on Equipment Management System Based on Deep Learning

In response to some issues such as delayed information processing, limited decision-support capabilities and insufficient situation awareness abilities in traditional equipment management systems, this paper constructs an equipment management system based on deep learning, aiming to improve the quality and efficiency of equipment management. Firstly, this article conducts a comprehensive analysis of system requirements and clarifies both functional and non-functional needs laying the foundation for system design. Secondly, this article designs the system's software and functional architectures based on Model-View-Controller (MVC)frameworks, Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks(CNN), and pre-trained models. Experimental verification demonstrates that the designed system performs well in terms of accuracy and reliability in equipment management. The research achievements provide more options for enhancing the scientific level of military equipment management.