Physics-informed neural network (PINN), featuring its merits in surrogate modeling and inverse analysis, provides a new paradigm for geo-engineering. PINN has been successfully applied in the simulation of continuum fields, such as solid mechanics, fluid mechanics, and thermodynamics. However, its application in fracture modeling also needs more attention since fracture simulation is an important and difficult problem in both scientific and engineering domains. This study adopts the partial differential equation of the phase-field fracture model as the physical constraint of PINN. We develop a staggered scheme for phase-field fracture modeling in 1D scenarios. Two networks are adopted for approximating fracture phase field and displacements, respectively. The results corroborate its correctness with analytical solutions. Losses of two neural networks are compared, and it is found that this multi-network method can effectively circumvent the problem of pathological gradients. It is also more promising for more complex problems such as multiphysics fracturing cases.