With the trend of using cloud computing to handle computation problems concurrently and reliably, various cloud computing platforms have emerged. In cloud computing, the fairness of multiple resource scheduling strategies is very important. Dominant resource fairness (DRF) can effectively implement the fairness in multiple resource environments, however, it can introduce uneven cluster load in the process of resource allocation. Therefore, we propose  a load-balanced dominant resource fairness allocation algorithm. It uses the DRF algorithm to allocate resources, perform K-means clustering on the slaves according to the resource utilization of each slave in the cluster, and allocate the resources to the tasks according to the clustering results to improve the load balance of the cluster. Simulations of the improved DRF algorithm are implemented on CloudSim4.0. Experimental results show that the proposed load-balanced DRF algorithm can  improve the load balancing of the cluster more effective than the original DRF algorithm and the improved DRF algorithm based on analytic hierarchy process (AHP).