Radars are commonly used in automotive applications to provide information on a target's location and velocity simultaneously. The multiple-input and multiple-output technology has been applied to improve spatial awareness, but impacts the Doppler sampling rate and causes velocity ambiguity. An incorrect velocity indication can pose significant safety risks in the automotive industry. To address this, we propose a complex-valued neural network to retrieve the actual velocity from aliased Doppler response by parsing signal magnitudes and phases. On the artificial data, it achieves an accuracy of 99.3%, outperforming the conventional methods on the same data.