The rational evaluation of carbon-based conductive ink performance is critical to both industrial production and applications. Herein, a model to evaluate writing performance of conductive ink by line resistance was proposed by investigating possible relations among different parameters and establishing relevant model to estimate ink writing performance. Bulk conductive inks were prepared and characterized to provide samples for model. To improve the precision of model, the impact of external factors including writing speed and angle was studied. Nonlinear regression and back propagation artificial neural network were employed to estimate line resistance, and cross check validation was conducted to prove robustness and precision of model. Most importantly, the investigation will open up a new path for the exploration of other carbon-based handwritten electronic devices.