Aiming at the problems of insufficient field test data of tractors and inaccurate realtime evaluation and prediction of unit performance and agronomy, a vehicleborne test terminal covering multiparameters and multiworking conditions was built, and a data platform for tractor operation load was established to obtain field operation load data of key parts of tractors. Based on this platform, the field operation load data of key parts and key parts of tractors were obtained. On this basis, the intelligent algorithm for reliable realtime prediction and evaluation of tractor traction performance was studied, which provided comprehensive basic data and effective prediction algorithm for product development, performance prediction and operation evaluation. Firstly, the operation parameters and structure system of the vehicle test terminal were introduced. Then, the tractor operation load data platform based on the field operation test nationwide was designed and built. Finally, based on the large agricultural data, the BP neural network and genetic algorithm were combined to classify and mine the basic working load of the data platform. The traction performance of tractor rotary tillage was predicted and evaluated. The results showed that the prediction accuracy of the neural network based on genetic algorithm was as high as 96.77%, and the root mean square error (RMSE) was less than 0.01, which showed that the prediction accuracy of the neural network based on genetic algorithm was as high as 96.77%. Neural network algorithm based on genetic algorithm can accurately and reliably evaluate and predict traction performance of tractor rotary tillage operation. 