Quality monitoring and early warning of water conservancy projects are vital links to guarantee the safe and stable operation of water conservancy projects. Due to some defects of early warning accuracy and warning response time, traditional system has some limitation; thus, we need to introduce more efficient method to improve system performance. This paper designs a water conservancy engineering quality monitoring and early warning system based on BP neural network. This system makes use of the nonlinear modeling and adaptive learning ability of BP neural network for real-time processing a large amount of monitoring data to extract the key information. Combining with real-time monitoring equipment and communication technology, the system realizes quick and stable data collection, transmission and processing, and through the optimization of the system, parallel computing to speed up calculation and decision-making, and its experimental results show that the system based on BP neural network can effectively improve the warning accuracy. The system can also learn from the monitoring data for many years, extract the data in the underlying potential pattern, and use them to model the early warning, which can further improve the accuracy of the early warning.