With the continuous improvement of the construction industry’s requirements for project quality control, traditional manual inspection methods can no longer meet the needs of modern construction projects for efficient and accurate quality inspection. Based on big data analysis technology, this study designs and implements an intelligent inspection system for real-time monitoring and identification of quality problems in construction projects. The system integrates multiple sensor data and uses advanced machine learning algorithms for data processing and analysis, which can automatically detect common problems such as cracks and deformations in building structures. The experimental results show that the system performs well in accuracy, real-time performance, and detection speed, and can maintain efficient response while processing large amounts of data, with an accuracy rate of 92.5% and a recall rate of 89.7%. The system can not only provide real-time feedback on building quality problems, but also provide timely decision-making support for project managers, greatly improving the intelligent level of construction project quality monitoring and has broad application prospects.