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During the operation of the seeder, the discharge device will experience non-stationary random vibration, which significantly affects seed discharge performance and holds great importance for acquiring and analyzing vibration signals. A denoising method was proposed that combined dragonfly algorithm (DA), variational mode decomposition (VMD), and wavelet threshold to continuously update the location and speed of dragonfly individuals through iterative processes. The optimal parameter combination for VMD decomposition effect was determined. A simulated random road signal in the time domain served as the initial signal and underwent denoising by using DA-VMD combined wavelet threshold, wavelet threshold denoising, empirical mode decomposition (EMD), VMD, and wavelet combined EMD methods respectively. The results demonstrated that the proposed method achieved superior denoising effects on non-stationary random vibration signals with post-denoising signal-to-noise ratio, root-mean-square value, and correlation number measuring 21.570, 0.094, and 0.833, respectively. Furthermore, vibration signals from seeders under different surface conditions and operating speeds during field seeding were collected and subjected to denoising by using the DA-VMD combined wavelet threshold denoising method. The effectiveness of denoising was evaluated based on smoothness index, signal energy ratio, and noise mode indices. The results indicated smoother signals with higher signal energy ratios after denoising across various working conditions.