Abstract: To address the issue of uneven fuel injection quantity among cylinders in high-pressure common rail diesel engines and the difficulty in directly measuring it, a cylinder-specific fuel injection quantity prediction model based on a physics-informed neural network（PINN） and a physical-data hybrid-driven approach — the physics-informed data-driven model（PDM） was proposed. Firstly, based on the hydraulic model of the fuel system and the crankshaft dynamics model, a simplified physical model establishing the relationship between the fuel injection quantity, the rail pressure, and the instantaneous engine speed was constructed. Secondly, a feedforward neural network（FNN） was designed, the deviation between the network and the physical model （physical loss） and the deviation between the network and the simulation data （data loss） were introduced as the loss function for network training. which was used for the error backpropagation learning of network parameters. Based on GT-SUITE one-dimensional simulation model, the prediction accuracy of the fuel injection quantity of the model was verified. The results showed that at rotational speeds ranging from 600 r/min to 1 500 r/min and rail pressures ranging from 60 MPa to 140 MPa, the root mean square error of the overall predicted fuel injection quantity by PDM was 2.114 5 mg, and R² was 0.990 4. When the number of training data(steady-state operating points) was reduced from 115 to 31, the R² of PDM on the test set still reached 0.967 8 which was higher than 0.374 3 of the pure data-driven model.