Reservoir computing is a machine learning paradigm with lower training costs that replaces traditional recurrent neural networks in some time series processing areas to simplify complexity. The compact network structure and low-complexity training method of this approach make it more suitable for hardware implementation, and reservoir computing exhibits unique advantages over other deep learning models. Memristor is a single device that can change its resistance state by memorizing the applied voltage or history current. The non-linear and time-memory characteristics of memristors are highly compatible with the dynamic properties required for reservoir computing. Consequently, memristors can be harnessed to construct nonlinear nodes within reservoirs, forming intricate dynamical units. This study introduces a novel type of reservoir building unit, termed as a memristor array in a random state, and proposes a reservoir computing hardware system based on memristor arrays in random states (MARS-RC). The randomness and nonlinear properties of this memristor array give the MARS-RC system the unique ability to more effectively capture the dynamic characteristics of the data. Simultaneously, we’ve designed an array random initialization circuit unit (ARI) to facilitate control over the memristor’s state, thus enhancing the system’s controllability. The MARS-RC system exhibits comparable predictive performance to conventional software reservoirs in predictive experiments involving chaotic time series. This is supported by comparisons with five distinct reservoir computing platforms. Furthermore, we’ve applied the MARS-RC system to the task of multi-classifying ECG signals. In these experiments, by employing multiple MARS-RC arrays in parallel, the system exhibits substantial robustness when dealing with varying input data sizes, attaining a remarkable classification accuracy of 99.375%. This study proposes a novel construction approach to advance reservoir computing hardwar...