Muscle–computer interfaces are devices that can identify the meaning of human bioelectrical signals, such as surface electromyography (sEMG) signals. sEMG signals can be obtained from arm-worn sensors and can be used to classify human gestures. In this paper, we propose a vector–kernel convolutional Takagi–Sugeno–Kang (TSK)-type neuro-fuzzy classifier (VK-CTNFC) to recognize human gestures represented by sEMG signals. First, vector–kernel convolution is used to extract the features of sEMG signals; this modification reduces the model parameters by half and increases the classification accuracy compared with those achieved with a conventional convolutional kernel. Second, the global average pooling method is used instead of the flattening method to improve feature fusion performance. Finally, a TSK-type neuro-fuzzy network is used for gesture classification. The publicly available dataset Ninapro DB1 was used in experiments for verifying the performance of the proposed VK-CTNFC. Data preprocessing was performed by wavelet denoising to smooth the sEMG waveform, and fast Fourier transform was used to convert time-domain sEMG signals into frequency-domain signals. Finally, the processed sEMG signals were input into the VK-CTNFC for training. The experimental results indicate that the proposed VK-CTNFC has an average accuracy of 87.18% and outperforms other reported methods.