To solve the problems of some Quality of Service (QoS)-based scheduling algorithms in cloud computing environment, such as slow optimizing speed and imbalance between scheduling cost and user satisfaction, a cloud task scheduling strategy based on clustering and improved SOS (Symbiotic Organisms Search) algorithm was proposed. Firstly, the tasks and resources were clustered by fuzzy clustering and the resources were reordered and placed, and then the tasks were guided and assigned according to the similarity of attributes to reduce the selection range of resources. Secondly, the SOS algorithm was improved according to the cross and rotation learning mechanism to improve the algorithm search ability. Finally, the driving model was constructed by weighted summation to balance the relationship between scheduling cost and system performance. Compared with the improved global genetic algorithm, hybrid particle swarm optimization and genetic algorithm, and discrete SOS algorithm, the proposed algorithm can effectively reduce the evolution generation, reduce the scheduling cost and improve the user's satisfaction. Experimental results show that the proposed algorithm is a feasible and effective task scheduling algorithm.