Object counting is an important research direction of computer vision. In order to solve the problem that the size of the object in the few-shot object counting is inconsistent with that in the query image, and the object distribution is uneven, a multi-scale feature enhancement counting algorithm is proposed. First, a top-down feature fusion network is constructed based on the feature pyramid. At each level of scale, the sample features of the regions with high sample similarity in the query image are enhanced, and then sent to the upper level for feature matching. The enhanced query features at all levels are sent into the regression head to obtain the density maps at all levels, and finally the sum can generate a high-quality density map. The experimental results on FSC-147 and CARPK datasets show that the performance of the model is better than that of most previous methods, which effectively improves the problem of low counting accuracy caused by the change of target size.