Engineering bidding management and risk prevention and control system based on cloud computing and smart contracts

This study aims to build an engineering bidding management and risk prevention and control system based on cloud computing and smart contracts, and propose an innovative risk prediction algorithm based on deep learning. The system adopts a layered architecture design, combining the powerful computing power of cloud computing and the automated execution characteristics of smart contracts to optimize the bidding management process. The algorithm implements multi-level feature extraction and adaptive learning through CNN, which significantly improves the accuracy of risk prediction. The experimental results show that the system effectively optimizes the bidding management process, the algorithm prediction accuracy reaches 92%, the recall rate reaches 88%, the mean square error is 0.03, and the absolute error is 0.05. Compared with traditional methods, the algorithm in this study shows significant advantages in accuracy, real-time and stability. The study not only enriches the theoretical system of engineering bidding management, but also provides efficient and accurate risk prevention and control tools for practical applications. Future research will further optimize the algorithm performance and expand the integration application with other technologies.