Abstract: Big data analysis has offered much greater statistical power around the world in recent years, particularly with the ever-increasing information technology, such as positioning, sensing, and mobile communication. In the field of agriculture, big data has also presented the promising potential to promote the terminals of the BeiDou Navigation Satellite System on agricultural machinery in China. However, the current management can be decentralized fail to form the nationwide operation of agricultural machinery using the big data system and service scheme, due mainly to the rapid increase of the data. In this study, a new big data system of BeiDou terminals was developed to compile, the data transmission specifications of platform-to-platform for the nationwide dynamic monitoring and quantitative statistical analysis of agricultural machinery operations in China. Three parts of the big data system were also installed in the agricultural machinery of the manufacturers. The first part was involved the agricultural machinery and the BeiDou/GNSS terminals. The second part was the Internet of Things (IoTs) platform for agricultural machinery for manufacturers. The third part was the big data management and service platform for the agricultural machinery operation by the BeiDou Team of China Agricultural University. Specifically, the BeiDou terminals were used to collect the position and working condition data of agricultural machinery. The gathered information was forwarded in real time to the manufacturers, then to the big data management and service platform. There were 290 153 sets of agricultural machinery in the big data system. Data mining and statistical analysis were performed on the big data management and service platform. The information products were generated to process the operation data on the big data management and service platform using three major procedures, including data cleaning, trajectory segmentation, and parameter extraction. In addition, the basic statistics were achieved, such as the working hours, driving mileage, and working area. Taking the trajectory data generated by the wheat harvester in North China from May 31 to June 26, 2021, as an example, a further examination was conducted to verify the applicability and efficiency of the big data system. The heat map and operation center of gravity transfer diagram in the harvesters were established to extract the harvesting parameters of time, efficiency, and harvesting area. The dependence on cross-regional harvesters was then determined in the main wheat-producing areas. It was found that there were 35 243 sets of online wheat harvesters during harvesting, with a daily average of 18 568 sets. The average daily harvesting duration was approximately 8.3 h, and the harvesting area was roughly 5.5 hm2/d. Besides, around 75% of the wheat harvesters were accomplished the cross-zone mechanized harvest, 69% of which presented a cross-zone distance (Euclidean distance) of more than 300 km, and an average of 597 km. More than 50% of the fields were harvested by the harvesters in Hubei and Hebei Province. The big data system was implemented the full connection with the 290 153 sets of agricultural machinery at the end of November, 2021. As such, the location and working condition data were integrated from the major manufacturers of agricultural machinery. The big data system can be widely expected to accurately evaluate the data processing and operation. The finding can also provide for the dynamic and real-time monitoring of agricultural machinery operation and data analysis services in modern agriculture.