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 self-updating confidence classification network (RPR-SCCN) was proposed. Firstly, a confidence classification network was constructed 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 the dots, decision evidence was constructed and corrected for fusion. The fusion result updated the dot category, and the updated dot drove the training and learning of the confidence classification network again. The optimized confidence classification network continued to perform the next round of trajectory confidence updates, decision evidence construction, and category label updated. This process iterated continuously until the radar trajectory category labels in the previous and subsequent rounds no longer changed. The experimental verification results of actual radar tracking show that the recognition accuracy of typical radar tracking intelligent recognition algorithms such as point fractal network (PF-Net), radar plot classification based on fully connected neural network (RPC-FNN), particle swarm optimization probabilistic neural network (PSO-PNN) and radar plot classification based on convolutional neural networks (RPC-CNN) is 82% ~ 90%, and the proposed algorithm can reach 93%, an improvement of 3% ~ 10%. In addition, the proposed algorithm has a small dependence on the number of training samples, making it easy to promote and apply.