Traditional pattern recognition systems, tasked with categorizing inputs into known classes, often struggle when they encounter samples they haven't been trained to recognize. This introduces the need for the open set recognition-enhancing models to reject unidentified samples effectively. The Openmax method represents a significant breakthrough in this field by leveraging deep learning to spot and handle these new, unseen classes, broadening the traditional Softmax layer to accommodate an "unknown" class and employing a single threshold to separate the known from the unknown. However, the reliance of the original Openmax method on a single threshold may result in incorrect classifications if the parameters are not selected appropriately. To address this, we introduce a dual-threshold fusion mechanism based on Dempster-Shafer evidence theory in this paper. This approach releases the difficulty of finding a precise threshold in the complex and dynamic real-world environments. By integrating deep networks with a novel evidence-based system, the refined approach can bolster the robustness of rejecting undefined classes.