Modeling multidimensional nonlinear complex dynamical systems remains a significant challenge due to inherent sensitivity, long-term dependencies, and computational complexity. Classical reservoir computing (RC) offers a powerful framework for temporal data processing but faces limitations in scalability and expressive power when dealing with computationally efficient scenarios or systems influenced by quantum effects. This paper investigates the potential of Quantum Reservoir Computing (QRC) as a novel paradigm for efficiently modeling and predicting the evolution of multidimensional nonlinear complex dynamics. Leveraging the natural evolution of quantum Ising model as a high-dimensional, nonlinear reservoir, QRC harnesses inherent quantum properties like superposition and entanglement to potentially enhance information processing capacity. We evaluate QRC’s performance on several representative complex dynamical systems characterized by strong nonlinearity. Through experimental analysis with different hyperparameter configurations, we validate the effectiveness of the edge of chaos and gllobal ergodic phase property in processing multidimensional nonlinear systems, thereby providing guidance for the selection of QRC hyperparameters. Our work provides algorithmic guidance for further promoting the practical deployment of QRC, which is expected to play a significant role in the research tasks of more complex dynamical systems.