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Improving the efficiency of cloud computing and reducing the energy consumption of data center is one of the main research contents in cloud computing. Particle swarm optimization (PSO) is often used to solve resource scheduling problems. However, in the application of cloud computing resource scheduling, PSO has fast initial convergence speed, slow convergence speed and easy to fall into local optimization. in this paper, we propose an adaptive improved particle swarm optimization algorithm for cloud computing resource scheduling problem. the algorithm improves the individual learning factors and social learning factors of particles by adaptive improvement, in order to improve the global exploration ability of the algorithm and make the particles approximate the better solution. The experimental results show that the proposed adaptive PSO not only has good convergence and global optimization ability, but also can greatly reduce the total completion time of task queue in cloud resource scheduling.