To address the problems in the application of lower limb exoskeleton mechanical equipment, XGBOOT Algorithm-based research on gait phase recognition is carried out, only using motion attitude data measured by a single IMU. Firstly, foot motion data of six different gaits are collected, and each gait is divided into four phases. On this basis, XGBOOT algorithm optimized is applied to analyze the gait phase recognition with the foot motion data as the training set. In the process of establishing the model, the parameters involved in the model are further optimized by the Bayesian optimization algorithm (BOA). Through calculation, the results show that the average accuracy of the model is 89. 26% in the verification set, the precision of the model is 89. 64% in the verification set, the recall rate of the model is 89. 26% in the verification set, F1 value of the model is 89. 10% in the verification set, which indicates that the model can achieve better gait phase recognition.