Gait assessment is one of the most important part of motor assessment of stroke. The clinical gait function assessment is subjective and based on kinematic results rather than neuromuscular changes. Therefore, a muscle synergy analysis method based on surface electromyography (sEMG) signals was proposed for quantitative assessment of gait function. The non-negative matrix factorization (NMF) algorithm was used to decompose the pre-processed multichannel sEMG signals. The synergy stability index (SSI) was obtained to describe the similarity of synergy structure vectors under different tasks in different groups. The between-group comparison showed significant differences in SSI between the healthy control and the patient’ s unaffected leg ( p = 0. 010) and between the SSI of the patient’s affected leg (p = 0. 007) when the tested leg was turned medially; the SSI of the healthy control leg was significantly different from the patient’s affected leg when the tested leg was turned laterally (p = 0. 036). The between-task comparison showed that SSI of the patient’s unaffected leg was significantly higher in the lateral turn than in the medial turn (p = 0. 017); the SSI of the patient’s affected leg during the lateral turning was significantly correlated with the lower extremity portion of the clinical motor function scale FMA_LE (r = 0. 671, r 2 = 0. 451, p = 0. 033). The experimental results suggested that SSI under the turning task can provide an objective and quantitative means of analysis for the assessment of clinical gait function, providing a new way for quantitative assessment from a neuromuscular perspective.