To solve the problems of insufficient feature extraction and ignoring time series information using convolutional neural network (CNN) to extract features of surface electromyography (sEMG) gesture recognition,resulting in low recognition accuracy,this paper innovatively proposed incorporating two-layer attention and multi-stream convolutional neural network (MS-CNN) for sEMG gesture recognition memory networks model.Firstly,sEMG images generated by sliding windows are used as the input of this model;then the channel attention module (CAM) is embedded in an MS-CNN to weaken irrelevant information and enable the network to focus more on the key features of sEMG;secondly,using the long short term memory network (LSTM) for motivating the input features in time-sequence to pay more attention to the time-sequence information of sEMG,which enables network has a stronger learning ability in the time-dimension;finally,focusing on states of LSTM by time-sequence attention (TSA) layer to learn important muscle information better for improving gesture recognition accuracy.Performing experimental tests on the NinaPro dataset and the results show that the gesture recognition accuracy in the DB1 dataset and DB2 dataset has reached 86.42% and 80.60% using the network model proposed by this paper,which is higher than most mainstream models, which fully verifies the effectiveness of the model.