This article aimed to explore a civil engineering quality monitoring system based on big data. Advanced data processing and analysis techniques were introduced in the experiment to solve the problems of traditional systems when facing large-scale and complex engineering projects. At the level of real-time monitoring, enhancing predictive performance, and promoting information integration and sharing, it is hoped that the overall quality management level of civil engineering projects can be improved. During the research phase of this article, three experiments were conducted to evaluate the performance of the system: accuracy evaluation experiments. The random forest algorithm was used to process simulated data. The experimental results showed that when the number of trees was 150, the accuracy of the model reached 85%; the precision reached 87%; the recall reached 82%. In the experiment of evaluating system data throughput and classification performance, when the dataset size was 10,000, 50,000, 100,000, 500,000, and 1 million, the system studied in this paper processed data in 2.3 s, 3.5 s, 4.2 s, 6.1 s, and 7.8 s, respectively. The accuracy improved from 90.5% to 96.3%, indicating that the system had good performance and high accuracy in processing big data. In the third practical application effectiveness evaluation experiment, for 50 engineering problems, the average delay time for the system to discover problems was 5 min, and the average time to solve problems was 15 min. In the three experimental results of this article, it can be seen that the monitoring system studied in this article has demonstrated good performance in accuracy, response time, and practical application effects, which can provide effective quality monitoring solutions for the field of civil engineering. How to further optimize the system is the key to improving the efficiency and accuracy of engineering monitoring.