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Aerial drone object recognition is an urgent demand in modern military and aviation fields. Due to the various functions and types of drones at present, it is difficult to collect a large number of new drone samples for training object recognition models. In order to solve this problem, a few-shot object recognition method of aerial drone based on model fine-tuning is proposed. The method is based on the Faster R-CNN architecture, the Faster R-CNN is pretrained by using common drone data with a large number of labeled samples. Then, the last classification layer of the infrastructure is replaced by cosine measurement, and a small balanced dataset combining new and common drones is constructed to fine-tune the classification layer with a small learning rate. Experimental results show that when the number of labeled samples is 5, 10 and 50, the mAP of few-shot object recognition model based on model fine-tuning is 88.6%, 89.2% and 90.8% respectively, which can meet the requirements of few-shot target recognition task of aerial drone, and is better than other few-shot target recognition methods.