The existing gradient sparsification compression technology still has the problem of large time consumption in practical applications. To solve this problem, a low-complex and high-speed approach based on the residual gradient compression algorithm in distributed training was proposed, to select the communication-set of the top-k sparse gradient. Firstly, the Wasserstein distance was used to determine that the characteristics of the gradient distribution conformed to the Laplacian distribution. Secondly, the key points were determined by the area relationship of the Laplacian distribution curve, and the feature parameters were simplified by maximum likelihood estimation. Finally, the sparse gradient top-k threshold was estimated and corrected by the binary search algorithm. The proposed approach avoided the instability of random sampling methods and some complex operations like data sorting. The CIFAR-10 and CIFAR-100 datasets were used to train the deep neural network for image classification on GPU platform in order to evaluate the effectiveness of the proposed approach. Results show that this approach accelerated the training process up to 1.62 and 1.3 times, compared with the radixSelect and the hierarchical selection methods under the same training accuracy.