Concerning the high time complexity problem of traditional F-X domain predictive filtering in suppressing random noise of seismic data, a parallel algorithm based on Compute Unified Device Architecture (CUDA) was proposed. Firstly, the algorithm was analyzed modularly to find the calculation bottleneck of the algorithm. Then, starting with the steps of calculating the correlation matrix, calculating the filter factor, filtering from each window data and so on, the filtering process was divided into multiple tasks for parallel processing based on the Graphic Processing Unit (GPU). Finally, the efficiency of the algorithm was improved by implementing the parallel algorithm and optimizing the redundant data reading in adjacent filter windows. Experimental results based on NVIDIA Tesla K20c show that in the seismic data of 250×250 work area, the proposed parallel algorithm achieves an efficiency improvement of 10.9 times compared with the original serial algorithm, while ensuring the calculation accuracy required in the engineering at the same time.