A dynamic workload description method and a dynamic workload matching algorithm were proposed to improve the accuracy of workload matching in automatic parameter tuning systems, such as OtterTune, with static workload description. First, a dynamic workload description method was proposed to describe the workload changes more accurately. Then, for the problems such as irregular sequences in workload matching and that the Euclidean distance algorithm is no longer applicable, a dynamic workload matching algorithm using data alignment was proposed based on the Dynamic Time Warping (DTW) algorithm. Finally, the proposed methods were applied to OtterTune to form a dynamic workload-based tuning tool D-OtterTune (Dynamic workload based OtterTune), and several experiments were conducted on it. Experimental results showed that, compared with the original method OtterTune, with the stable improvement in the workload matching accuracy for automatic parameter tuning brought by the dynamic workload matching method, D-OtterTune had the accuracy increased by 3%. It can be seen that D-OtterTune can have a significant impact on the overall business performance in data-intensive applications.