In order to improve the accuracy of gait pattern identification for wearable sensor data, we propose a new model of deep learning-based gait pattern identification, which combines a convolutional neural network with a long short-term memory neural network in this study. This model makes full use of the local spatial features of data obtained by a convolution neural network and the excellent correlation of intrinsic feature time of data obtained by a long short-term memory neural network model. It can effectively mine the temporal-spatial gait features implied by high-dimensional, nonlinear, and random temporal gait data of wearable sensors which are closely related to gait pattern changes, improving the model’s classification performance of gait patterns. The UCI HAR data set from University of California is used to validate the proposed model. The experimental results show that the model can effectively collect temporal-spatial gait features embedded in the gait data of wearable sensors. This can reach classification accuracy of 91.45%, precision of 91.54%, and recall of 91.53%, signaling significantly better classification performance than that of traditional machine learning models, which serves as a new scheme for the accurate gait pattern identification of wearable sensor data.