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Aiming at the problems of poor generality and low prediction accuracy of existing models for traction performance of wheeled tractors, a set of general model for traction performance prediction of four-wheel drive and two-wheel drive tractors was proposed, which covered the whole process of system modeling, prediction optimization and case verification. By analyzing the interaction of many physical fields such as soil mechanics, tire mechanics and transmission system, the tractor traction performance was abstracted into four basic models, namely wheel-soil model, driving force model, slip rate model and tractive force model, in order to establish a general model for the whole machine traction performance prediction of four-wheel drive and twowheel drive tractors. In order to improve the prediction accuracy, the traction performance prediction optimization algorithm based on adaptive particle swarm optimization (APSO) was established with the overall machine slip rate as the optimization objective. Through on-line optimization, the accuracy and universality of the model were verified. In order to further verify its superiority and engineering practicability, a 105kW tractor of YTO was used as a test prototype to complete the offline test in the whole field test site. The experimental results showed that compared with the existing prediction models, the error of slip rate and rolling resistance of the APSO-based prediction method was 1.9% and 0.18kN, respectively. For two-wheel drive tractors, the corresponding errors were 2.7% and 0.25kN, respectively, and the accuracy was greatly improved.The general model of traction performance prediction for four-wheel drive and two-wheel drive tractors was studied, which had certain research significance in the fields of traction control and performance of wheeled tractors.