为了提高对下肢运动的分类准确度，提出了一种基于表面肌电信号（surface electromyography, sEMG）的卷积神经网络与长短期记忆网络融合识别模型（convolutional neural network and long short-term memory network, CNN-LSTM）。首先，采集了20名受试者进行上楼、下楼、行走和蹲起4种步态动作的sEMG；接着，对采集到的sEMG数据进行预处理，并提取了两种时域和频域特征，用作机器学习识别模型的特征输入；最后，基于预处理后肌电信号数据，构建了CNN-LSTM的下肢动作识别模型，并与CNN、LSTM和支持向量机（support vector machine, SVM）模型的性能进行对比。结果显示，CNN-LSTM模型在下肢动作识别准确率上分别比CNN、LSTM和SVM模型高出2.16%、8.34%、和11.16%，证明了其优越的分类性能。研究结论为康复医疗器械与助力器械提供了一个有效的下肢运动功能改善方案。

To enhance the classification accuracy of lower limb movements, this paper was introduced a hybrid recognition model based on surface electromyography (sEMG) that combines convolutional neural networks (CNN) with long short-term memory networks(LSTM). Initially, sEMG data were collected from 20 subjects performing four types of gait movements: ascending stairs, descending stairs, walking, and squatting. Subsequently, the collected sEMG data underwent preprocessing, and both time domain and frequency domain features were extracted to serve as inputs for the machine learning recognition model. The CNN-LSTM model was then constructed for lower limb action recognition and compared against the performances of CNN, LSTM, and SVM (support vector machine,) models. The results demonstrate that the CNN-LSTM model outperforms the CNN, LSTM, and SVM models by 2.16%, 8.34%, and 11.16% in accuracy, respectively, thereby proving its superior classification performance. This model provides an effective solution for enhancing lower limb motor functions, offering significant benefits for rehabilitation medical equipment and power assist devices.