Objective To reveal the gait pattern differences between higher-mileage runners ( HMR) and low-mileage runners ( LMR) by using the deep neural network ( DNN) classification model, and investigate the interpretability analysis of successfully recognized gait patterns by layer-wise relevance propagation ( LRP) technique. Methods Through DNN, 1 200 groups of gait feature data from HMR and LMR were trained and classified. Then, the LRP was used to calculate the relevance score ( RS) of relevant variables at each time point, and the high relevance variables were extracted to analyze the interpretability of gait pattern differences. Results The DNN model achieved 91. 25% accuracy in gait feature classification between HMR and LMR. The contribution of variables during 1% -47% stance phase was higher than the contribution of variables during the 48% -100% stance phase to the successful classification. The sum contribution rate of the ankle joint related trajectory variable RS reached 43. 10% , and that of the knee joint and hip joint was 37. 07% and 19. 83% , respectively. Conclusions The ankle and knee provide considerable information can help recognize gait features between HMR and LMR. The early stages of the stance are very important in the term of gait pattern recognition because it may contain more effective information about gait patterns. LRP completes a feasible interpretation of the predicted result of the model, thus providing more interesting insights and more effective information for analyzing gait patterns.