The surface electromyography signal can reflect the user′s action intention. Therefore, it becomes the main control signal for human-computer interaction. However, the individual variability makes the user model universally un-applicable, which is not conducive to the development of the universal EMG equipment. In this paper, from the perspective of neural synergy control, muscle synergy is extracted by the non-negative matrix factorization algorithm. Then, the pre-experimental data of new user are combined with least squares to obtain training synergy as a feature quantity, which is similar to pre-experimental synergy. For application consideration in low-frequency wearable scenarios, three simple and easily portable classifiers ( i. e. , support vector machine, error back propagation network, and K-nearest neighbor algorithm) are trained and tested, respectively. Four sets of gesture recognition experiments are implemented in DB1 ( 100 Hz) and DB5 ( 200 Hz) of the Ninapro database. The average recognition accuracy rates are 81. 12% , 78. 19% , 74. 07% , 60. 11% ( DB1 ) and 85. 75% , 83. 25% , 79. 07% , 66. 10% ( DB5 ), which are higher than the existing low-frequency online recognition algorithms by more than 10% . The proposed algorithm is simple and easy to train the classifier using existing user data and a small amount of pre-experimental data from new users. Meanwhile, the action intention can be judged from the perspective of neural coordination, which is more conducive to the development of a control method that conforms to the natural movement of the human body. It provides a feasible solution for the popularization of wearable electromyography equipment.