Gait recognition of lower limb exoskeleton is a key technology to realize human-machine cooperative control, however, the existing gait recognition methods face the challenges of insufficient efficiency of local feature extraction, weak generalization ability of small samples, and high computational overhead of the model when dealing with one-dimensional time series data. Aiming at the above problems, this paper proposes a hybrid model based on 1D-CNN-SVM, which automatically extracts local features of 1D time-series data by a 1D convolutional neural network (1D-CNN) and realizes highly robust classification under small-sample conditions by using support vector machine (SVM). The experimental results show that the model achieves an overall recognition rate of 99.00% on the customized gait dataset, which is 5.67% and 7.99% higher than the traditional SVM model and the single 1D-CNN model, respectively. In addition, the number of parameters of this model is only 26 156, and the single-sample inference time is as low as 0.06 ms, which is significantly better than the hybrid 1D-CNN-LSTM model. This study provides a solution for gait recognition of lower limb exoskeleton that still combines generalization ability, recognition ability and light weight under small sample conditions.