In order to solve the problem that the pulsar recognition research is limited to the field of conventional classification algorithm, the research ideas are mainly divided into two categories: data adaptation model and model adaptation data, and the lack of pertinence is the problem, to study the characteristics of the pulsar dataset, excavates the correlation between its intrinsic characteristics and other research fields, and finds that there is a connection between pulsar data and long-tail distribution. Aiming at the limitations of the field of pulsar data identification, combined with the characteristics of pulsar data itself, in this study. The consistency of pulsar data with the long-tail distribution is explored, and for the first time, the pulsar data distribution is regarded as a special case of long-tail distribution. On this basis, from the perspective of optimized training strategy from the perspective of long-tail visual recognition, a pulsar recognition algorithm based on decoupling training strategy is proposed. The traditional algorithm in the field of pulsar recognition is mainly improved from the idea of optimizing the model and data, compared with the traditional algorithm, the algorithm proposed starts from the training point of view, adopts the decoupling training strategy, and is simple and efficient in operation, with stronger portability. The training process of the algorithm is divided into two stages, the first stage is the joint training of the overall pulsar recognition model, and the sampling strategy is sampling based on the balance of the instances; The second stage is to fix the feature extraction network on the basis of the first stage, and fine-tune the classifier. A variety of different fine-tuning strategies were used, including class balancing training on classifiers and normalization of features to find nearest neighbors. After the verification of multiple data sets, the individual fine-tuning of the classifier can effectively improve the decision-making boundary and improve the recall rate and other indicators, which is a cost-effective improvement method.