In the task of few-shot open-set recognition (FSOSR), effectively distinguishing closed-set from open-set samples presents a notable challenge, especially in cases of sample scarcity. Current approaches exhibit uncertainty in describing boundaries for known class distributions, leading to insufficient discrimination between closed-set and open-set spaces. To tackle this issue, this study introduces a novel method for FSOSR leveraging feature decoupling and openness learning. The primary objective is to employ a feature decoupling module to compel the model to decouple class-specific features and open-set features, thereby accentuating the disparity between unknown and known classes. To achieve effective feature decoupling, an openness learning loss is introduced to facilitate the acquisition of open-set features. By integrating similarity metric values and anti-openness scores as the optimization target, the model is steered towards learning more discriminative feature representations. Experimental results on publicly datasets miniImageNet and tieredImageNet demonstrate that the proposed method substantially enhances the detection rate of unknown class samples while accurately classifying known classes.