The educational resource management based on image data visualization and deep learning

In order to address issues such as inaccurate education resource positioning and inefficient resource utilization, this study optimizes the Educational Resource Management System (ERMS) by combining image data visualization techniques with convolutional neural networks (CNNs) technology in deep learning. Firstly, the crucial role of ERMS in education and teaching is analyzed. Secondly, the application of image data visualization techniques and CNNs in the system is explained, along with the associated challenges. Finally, by optimizing the CNNs model and system architecture and validating with experimental data, the rationality of the proposed model is confirmed. Experimental results indicate a significant improvement in various performance metrics compared to traditional models. The recognition accuracy on the Mnist dataset reaches 98.1 %, and notably, on the cifar-10 dataset, the optimized model achieves an accuracy close to 98.3 % with improved runtime reduced to only 640.4 s. Additionally, through systematic simulation experiments, the designed system is shown to fully meet the earlier requirements for system functionality, validating the feasibility and rationality of the model and system in this study. Therefore, this study holds high practical value for optimizing ERMS and provides meaningful insights into image data visualization techniques and CNNs optimization.