Operation Decision and Optimization System of Network Enterprise based on Digital Intelligence Algorithm

Intelligent algorithms have broad application prospects in enterprise operation decision-making. This study theoretically explores intelligent algorithms and designs a financial early warning system based on particle swarm optimization. The system has high prediction accuracy and practicality, which can help enterprises avoid risks and improve efficiency. By analyzing financial data and calculating models, it provides financial risk warning for enterprises, thereby optimizing their operational decisions. From the experimental data, it can be seen that the financial prediction results provided by the system in this study have a small deviation from the actual value, while the financial revenue and expenditure prediction accuracy of the latter based on risk control early warning system is poor, with a maximum difference of 30.462. Moreover, particle swarm optimization algorithm has advantages such as fast convergence, easy implementation, and strong global search ability, making it more stable.