With the development of urban rail transit engineering construction, higher requirements have been put forward for the statistics of engineering quantities and the preparation of budget estimates in project engineering economy. The realization of intelligent applications in this field is in line with the requirements of automation and intelligence in application scenarios, and is in line with the development requirements of the urban rail transit industry, which is conducive to significantly improving productivity. By integrating multiple data sources, including but not limited to existing engineering quantity data, budget estimate data, changes in fixed price, and other information, and applying advanced data processing algorithms and technologies, the adaptability of different algorithms, the influence of parameters, and the correlation of different variables are studied to realize the analysis and prediction of various indicators of engineering projects. By introducing the big data analysis model into the calculation of engineering quantities and the preparation of budget estimates, not only the accuracy and efficiency of budget estimate preparation can be effectively improved, but also a basis can be provided for the optimization of engineering projects. With the accumulation of more data and the advancement of technology, the platform is expected to be further improved and become an important tool to support the management of urban rail transit projects.