In the field of gesture recognition based on surface electromyographic (sEMG) signals, many deep learning methods are able to achieve high recognition accuracy on multiple gestures. However, in practical applications, sEMG gesture recognition is susceptible to interference from irrelevant gestures, leading to a decrease in recognition accuracy and affecting system stability. Therefore, identifying the category of target gestures while excluding interference from irrelevant gestures has become a hotspot issue in this field. This paper introduces a gesture recognition algorithm based on a hybrid classifier under non-ideal conditions. Firstly, we introduce and improve the multi-class classifier LST-EMG-Net to enhance the algorithm’s accuracy on target gestures. Secondly, we introduce EMG-FRNet as a single-class classifier to improve the algorithm’s accuracy on irrelevant gestures. Then, a feature correlation screening module is designed to intercept target gesture samples outside the single-class classifier, avoiding misjudgment by the single-class classifier as irrelevant gestures, thus enhancing the overall accuracy of the algorithm on target and irrelevant gestures.