The vehicle traffic scheduling problem at unsignalized intersections is the research focus in the field of intelligent transportation. Because the solution space of vehicle traffic order decision problem increases exponentially with the increase of the number of vehicles, finding the optimal traffic sequence while ensuring the real-time performance has become a major problem for traffic scheduling at unsignalized intersections. To solve this problem, this paper proposed a vehicle traffic scheduling method at unsignalized intersections based on adaptive Monte Carlo tree search algorithm, which used a hierarchical framework, upper level centralized sequential decision-making, and lower level distributed trajectory planning. Firstly, the intersection model based on the conflict point was established, the connected vehicles were added to the queue to be searched, the Monte Carlo tree search process of the traffic sequence was designed according to the vehicle traffic characteristics in the intersection, the evaluation function was established with the total traffic time as the index, and then the adaptive exploration coefficient and other super parameters were designed for different traffic situations, so that the algorithm could maintain the best state in solving different vehicle numbers and different search periods. In the trajectory planning process, the acceleration two norm was taken as the objective function, and the speed, acceleration and the position of the starting point were taken as constraints to establish the optimal control proposition to solve the vehicle trajectory. Finally, experiments were carried out, and the results show that compared with other algorithms, the maximum optimization amplitude of this algorithm in numerical simulation and micro platform experiment is 33.42% and 38.04% respectively, which provides an effective solution for vehicle traffic scheduling at unsignalized intersections.