As the scale of tasks in the cloud environment continues to expand, the problem of high energy consumption in cloud computing centers has become increasingly prominent. In order to solve the problem of task assignment in a cloud environment and to effectively reduce energy consumption, a Modified Particle Swarm Optimization algorithm (M-PSO) was proposed. First, a cloud computing energy consumption model, which takes into account the processor's execution energy consumption and task transmission energy consumption, was introduced. Based on the model, the task assignment problem was defined and described, and the particle swarm optimization algorithm was used to solve this problem. In addition, a dynamically adjusted inertia weight coefficient function was constructed to overcome the local optimization and slow convergence problem of the standard PSO algorithm, and the strategy can effectively improve the system performance. Finally, the performance of the introduced algorithm model was evaluated by simulation experiments. The results show that the M-PSO algorithm can effectively reduce the total energy consumption of the system compared with other algorithms.