When multiple radars collaborate for target detection and recognition, the obtained data is rich in clutter and uncertain information due to the complex battlefield environment. Traditional radar plots recognition algorithms have certain limitations in processing such data. Therefore, a radar plots recognition algorithm based on adaptive confidence classification network (RPR-ACCE) has been proposed in this paper. Firstly, construct a confidence classification network to obtain the belief of each radar plots belonging to target, clutter, and uncertainty that under each iteration. Then, based on the spatial distribution characteristics of these plots, decision evidences are constructed and corrected for fusion. The fusion result updates the class label of the plots, and the updated plots also drive the training of the confidence classification network again. This iterative optimization is carried out until the class labels of all radar plots are no longer updated. Experiments based on measured radar plots show that compared with traditional typical radar plot recognition algorithms, the new algorithm can effectively improve the accuracy of plot recognition. In addition, the dependence on training samples is relatively small, making it easy to promote and apply to other scenarios.