【Purpose】In order to solve the problem of image classification models lacking open set generalization due to traditional closed set training methods when facing open set recognition problems, we propose a separate independent classification network structure.【Method】Each category contains an independent linear feature layer. The neural nodes designed in the feature layer can capture the category features more accurately under limited data samples. At the same time, a class of negative samples without labeling is introduced in the model training, so that the model not only relies on the feature difference of the known categories when constructing the decision boundary, but also increases the open set generalization of the model decision boundary without adding additional labeled samples.【Result】The results show that both the ICOR model structure and the open-set adaptive training strategy can effectively improve the OSR performance of traditional models; with the increase of openness, it can demonstrate better robustness; can more effectively reduce the OSR risk of the model. 【Conclusion】The proposed independent classification network combined with open-set adaptive training algorithm has better open-set recognition performance than existing open-set recognition algorithms.