Gait phase classification plays a crucial role in diagnosing walking impairments and guiding rehabilitation training. In recent years, various deep learning models have been proposed to classify gait phases by automatically learning features from temporal data. Many of these models rely on recurrent neural networks (RNNs) to capture the temporal continuity inherent in gait data. However, existing methods often struggle to model long-term dependencies and the subtle phase transition features that are essential for capturing the dynamic variations of gait. To address these limitations, we propose a novel multilayer context network (MLCNet), which integrates a multi-head contextual learning module to extract long-term dependency features and a squeeze-and-excitation (SE) module to capture adjacent phase transition features. This hybrid design enhances the model's ability to represent the dynamic characteristics of gait. We evaluate the performance of the proposed model on the publicly available gait dataset GEDS. The experimental results show that our method achieves an overall accuracy of 89.6%, an MF1 score of 69.1, and a kappa coefficient of 0.70. MLCNet effectively classifies four gait phases—heel-strike (HS), toe-strike (TS), heel-off (HO), and toe-off (TO)—with high sensitivity to dynamic gait features. These results indicate that MLCNet can assist clinicians in accurately diagnosing abnormal gait patterns and improving rehabilitation outcomes.