To ensure the safety of blind and visually impaired patients and reduce false alarms in the process of obstacle avoidance, a walking auxiliary system based on gait detection algorithm is designed. In the signal processing algorithm, wavelet packet is used to reduce the noise of the signal, the mean value, standard deviation, variance and wavelet energy of the three-axis acceleration are extracted, and multi-dimensional parameters are constituted as the gait characteristics by combining the information of foot pressure. Then, the optimized multi-classification support vector machine algorithm based on genetic algorithm is selected to train and identify the gait. The hardware part mainly consists of three-axis acceleration signal and plantar pressure signal acquisition hardware design. The software part mainly includes the acquisition and processing of the plantar pressure signal and three-axis acceleration signals and an android phone APP which can dial automatically. Experimental results show that the proposed system is based on the gait detection technology of multi-sensor fusion, with an average gait recognition rate of 90.48%. The system has good portability, low power consumption, good obstacle detection effect, and has certain research significance and practical value in the field of auxiliary walking.