Optimization of Research Management Digital Platform Based on Improved Convolutional Neural Network

The rapid increase in the amount of scientific research data and the increase in management complexity have made traditional scientific research management methods difficult to meet the needs of modern scientific research. There is an urgent need to apply advanced computer technology, especially deep learning technology, to achieve the digitization and intelligence of scientific research management. This article proposed a method based on an improved Convolutional Neural Network (CNN) model for optimizing research management digital platforms. This article first identified key data and process nodes in scientific research project management by analyzing existing scientific research management processes; then, an improved convolutional neural network model was designed to enhance feature extraction capability and optimize data processing flow, improving the accuracy and speed of data analysis. In addition, the model was integrated into a user-friendly scientific research management platform, which can monitor the progress of scientific research projects in real-time, automatically process a large amount of scientific research data, and provide intuitive data visualization services. From a budgetary perspective, the computing area has the highest budget at 900,000 yuan, reflecting the complexity and high cost of computing research. The pedagogy area follows closely with a budget of 800,000 yuan, which demonstrates the depth and breadth of pedagogical research. The research demonstrates how deep learning and data science, supported by computer technology, can drive the digitization of research management, providing new tools and ideas for the future development of the field.