Based on multiple kernel learning (MKL) support vector machine and decision tree combined strategy, a multi-class classification method is proposed to classify lower limb motions using electromyography (EMG) signals. According to the framework of multiple kernel learning, the MKL-based multi-classifier is constructed using binary tree decomposition method. Four-channel surface EMG signals are firstly collected from lower limb muscles, and then some time-domain features are extracted and inputted into the proposed multi-classifier. Five subdividing patterns are finally identified in level walking, i.e. support prophase, support metaphase, support telophase, swing prophase and swing telophase. The experimental results show that the proposed method can successfully identify these subdividing patterns with better accuracy than standard single-kernel support vector machine classifier.