Physics-informed neural networks (PINN) is applied to solve the fluid dynamics problems. It reconstructs regional or whole flow fields under sparse data conditions by embedding governing physical equations. However, conventional PINN models usually employ a single shared output structure for multiple physical variables, which may introduce cross-variable coupling errors among pressure and velocity components. This design can introduce cross-variable optimization interference. In addition, their time series extrapolation capability is often limited when dealing with unsteady flow fields. To address these limitations, a multi-output physics-informed neural network (MO-PINN) is developed here, for the reconstruction and time series prediction of unsteady flow fields. In the proposed MO-PINN framework, separate output channels are assigned to different physical variables, allowing each channel to be optimized independently. Such a multi-channel architecture effectively alleviates the coupling among physical variables, thereby enhancing the overall prediction accuracy. Specifically, the spatiotemporal coordinates are first fed into shared feature layers to extract the common latent representation of the flow field. Then, independent output channels are constructed for pressure and velocity components. Each output channel maps the shared features to a specific physical variable and is optimized separately. By reducing coupling interference among different physical quantities, the proposed architecture improves the capability of flow field reconstruction and prediction. The proposed MO-PINN is applied to the flow field reconstruction and time series prediction of two-dimensional flow past a circular cylinder. The numerical flow field data are obtained from computational fluid dynamics simulations and used to evaluate the reconstruction accuracy and temporal prediction performance of the model. Comparative analyses are conducted between the PINN and the proposed MO-PINN. The results show that MO-PINN can more accurately reconstruct the spatial distribution of pressure and velocity fields and better capture the unsteady wake characteristics behind the circular cylinder. In time series prediction, MO-PINN also shows improved prediction stability and temporal continuity. Quantitative results demonstrate that, relative to the baseline PINN, the reported accuracy gains are 42.9% for flow-field reconstruction and 26.9% for time-series prediction, indicating a significant improvement in prediction performance. These results demonstrate that the proposed multi-output architecture can effectively reduce coupling interference among physical variables and provide a feasible method for improving unsteady flow field reconstruction and prediction using PINN.