The primary source of the bias of atmospheric motion vectors (AMVs) is related to inaccuracies in assigning heights. The cloud top height algorithm employed by China’s new generation geostationary meteorological satellite FY-4A make use of both the infrared window channel and CO2 slicing channel. This leads to an improvement in its accuracy. In this study, cloud top height is used to correct AMVs of FY-4A satellite through spatiotemporal matching between the two products. Representative pixels within the AMVs tracking box are searched, and their average cloud top height is used to replace the original cloud height of the AMVs, achieving height reassignment. Verification using ERA5 reanalysis data shows that after height reassignment, the root mean square error (RMSE) of FY-4A infrared channel AMVs is significantly reduced across high, medium, and low layers. Specifically, the RMSE for the high layer decreases from 4.06 m·s-1 to 3.25 m·s-1, for the medium layer from 4.25 m·s-1 to 3.71 m·s-1, and for the low layer from 2.42 m·s-1 to 2.13 m·s-1. Height reassignment alleviates the problem of AMVs being assigned to overly high altitudes, reducing biases, particularly improving slow motion biases. Case studies of the Northeast China cold vortex and Typhoon Chaba demonstrate that this method can improve the consistency between cloud-derived winds and the background field. Promising applications of this method in numerical weather prediction assimilation and weather process analysis are envisioned.