This paper constructs a wavelet-gated temporal convolutional network model. First, the input electromyographic signals are subjected to multi-level discrete wavelet decomposition through a wavelet convolution module, and the components of each level are respectively subjected to one-dimensional convolution. Then, the detailed coefficients and approximate coefficients after convolution are reconstructed via discrete inverse wavelet transform. This process of multi-level decomposition, convolution, and step-by-step reconstruction enables the model to adaptively focus on key time-frequency features. The reconstructed signals are then input into a temporal convolutional network integrated with a gating unit. The proposed network structure achieves an accuracy of 81.85% for 52-class gesture classification on the Ninapro DB1 dataset, which is 4.9% higher than that of traditional temporal convolutional networks. Compared with recent mainstream deep models in this field (such as MSHilbNet, GengNet, etc.), this method achieves a relative accuracy improvement of 4.0%~7.8% while maintaining a smaller number of model parameters.