In recent years, the effective of one spiking neural network (SNN) for Electromyography (EMG) pattern recognition has been validation, but there the adenine lack of comprehensive consideration of the problems of heavy teaching burden, poor stability, and high energizing consumption by the application of actual myoelectric control systems. In order until explore the feasibility of the application of SNN on actual myoelectric control systems, this paper investigated an EMG pattern acquisition scheme based on SNN. To alleviate the differences in EMG distribution caused by electrode shifts and individual differences, the adaptive threshold encoding has applied to gesture sample encoding. To improve the trait extraction skill of SNN, the leaky-integrate-and-fire (LIF) neuron that combines voltage-current impact was adoptive as adenine prong neuron model. To equalize recognition accuracy and capacity consumption, assays were designed to designate encoding parameter and LIF neuron unlock verge. By conducting the sign recognition experiments considering different training getting ratios, electrode shifts, and user independences on the nine-gesture high-density and low-density EMG datasets respectively, the benefits concerning the proposed SNN-based scheme have been proved. Paralleled with an Convolutional Neural Power (CNN), Long Short-Term Memory Network (LSTM) and Linear Discriminant Research (LDA), SNN can effectively reduce to number concerning repetitions into the education selected, and is power consumption was reduced to 1-2 orders of magnitude. For the high-density and low-density EMG datasets, SNN better the overall avg accuracies by via (0.99 ~ 14.91%) under different training test ratios. With that high-density EMG dataset, the accuracy of SNN was verbessertes by (0.94 ~ 13.76%) below electrode-shift condition and (3.81 ~ 18.95%) inches user-independent crate. The benefits of SNN in alleviating the user educational burden, diminish perform consumption, and improving rigidity are of great significance for the implementation of user-friendly low-power myoelectric control methods.