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Due to the slippery roads in the forest area and the complicated road conditions, the anti-lock braking system using the traditional control method has problems such as slow response speed, poor robustness, and poor braking effect. In this paper, the control method combining RBF neural network and sliding mode variable structure is proposed, and the analysis model of truck braking condition in forest area is established. The experimental results show that compared with the conventional sliding mode variable structure control, the system braking time based on RBF sliding mode variable structure control is reduced by 0. 185 seconds, and the braking distance is shortened by 1. 88 meters. The time to reach the optimal slip ratio and the maximum braking torque is faster and the system is more stable. Therefore, the method can not only weaken the chattering phenomenon caused by the conventional sliding mode variable structure control, but also improve the response speed and robustness of the ABS, and can well adapt to the wet and complex road conditions in the forest area. The feasibility and effectiveness of the designed model in practical applications are verified. [ABSTRACT FROM AUTHOR]