Gait recognition is a key technology for lower limb exoskeleton robots, and accurate gait recognition plays a crucial role in the flexible control of these robots. To address the randomness in gait characteristics (such as walking speed and stride length) across different individuals and within the same individual, this paper proposes a gait phase recognition method based on an improved SECBAM-Densenet network model.Firstly, two inertial measurement units were placed on the tibia and the rectus femoris muscle of the thigh to collect gait data from 200 participants performing four gait tasks: walking forward, turning, ascending stairs and descending stairs. After filtering and resampling the data for preprocessing, the processed data were used as input to the proposed model. Finally, the SECBAM-Densenet model was used to classify the gait phases. The results show that the improved SECBAM-Densenet model achieved an average recognition accuracy of 95.76% in different gait phases within the same individual, which represents an improvement of 0.66% to 21.22% compared to other models. For different individuals, the recognition accuracy for each phase was higher than 94%.These experimental results indicate that the proposed model can be applied in the field of gait phase recognition, providing experimental reference for the flexible control of lower limb exoskeleton robots.