Gait phase recognition is an effective method of analyzing human motion and behavior that can be very meaningful in people’s daily life, especially when struggling with assisted rehabilitation. In this paper, a new algorithm that can recognize a human gait phase more accurately is proposed. The new gait phase recognition algorithm is based on a deep memory convolutional neural network (DM-CNN) using multiple sensor fusion. We used the plantar pressure sensor array and acceleration sensor array gait data, and then extracted the gait features using the DM-CNN. The measured data of the continuous gait cycle were divided into unit steps, and the data were analyzed and preprocessed. Then, a feature map of each sensor array was extracted by constructing a separate DM-CNN. Finally, each feature map was combined into a fully connected network, and a memory function was introduced to simulate historical behavior. We then tested the algorithm on the phases of a gait cycle and compared the evaluation indicators of each phase. In the experiment, we compared single-mode and multimode recognition results, and compared those with the new hidden Markov model (N-HMM), K-nearest neighbor (KNN), and hidden Markov model (HMM) algorithms. The experimental results show that when the multisensor data are fused, the average recognition accuracy can reach 97.1%, which is higher than those of the other algorithms and improves the recognition of a human gait phase. The accurate recognition of human gait can provide a better theoretical basis for the design of exoskeleton robot control strategies.