The oxygen vacancies in the TiOx active layer play the key role in determining the electrical characteristics of TiOx–based memristors such as resistive-switching behaviour. In this paper, we investigated the effect of a multi-layer stacking sequence of TiOx active layers on the resistive-switching characteristics of memristor devices. In particular, the stacking sequence of the multi-layer TiOx sub-layers, which have different oxygen contents, was varied. The optimal stacking sequence condition was confirmed by measuring the current–voltage characteristics, and also the retention test confirmed that the characteristics were maintained for more than 10,000 s. Finally, the simulation using the Modified National Institute of Standards and Technology handwriting recognition data set revealed that the multi-layer TiOx memristors showed a learning accuracy of 89.18%, demonstrating the practical utilization of the multi-layer TiOx memristors in artificial intelligence systems.