In the study of prosthetic control techniques, researchers usually decode surface electromyography (sEMG) to obtain the amputee’s intention of motion. The traditional sEMG electrode usually requires direct contact with the skin by conductive paste to reduce the impedance between the skin and the electrode, which may cause skin allergies and physical discomfort. sEMG is also easily affected by muscle fatigue, which is inconvenient in long-term monitoring. To address the above issues, this study used a nano-gold flexible sensor to decode the deformation signal generated by muscle contraction and explored the classification performance of two different training modes. The first mode was the sequential training mode, where each action was repeated three times, and the second one was the random training mode, where the order of actions was randomized, and each action only appeared once. The results show that the average gesture recognition rate of all subjects is above 90%, and there is no significant difference between the two training modes (sequential training mode is 95.46%, random training mode is 94.18%, P-value is 0.227 5). The experimental results demonstrate that the nano-gold flexible sensor, like the wet electrode, enables reliable gesture recognition.