Conformal metasurfaces with flexible structures can fit complicated platforms and have obvious advantages in moving platforms scattering manipulations. However, the far-field simulation of conformal metasurface is time-consuming and difficult to optimize, thereby making the its agile design difficult. Here, an efficient and intelligent scattering field calculation method is proposed based on transfer learning for conformal metasurfaces. Firstly, according to the consistency in physical mechanism between antenna theory and full wave simulation, an initial mapping model between phase distribution and far-field of metasurface is constructed and pre-trained based on a large quantity of theoretical data in source domain. Secondly, by pre-training, parameter freezing and model fine-tuning, the far-field prediction model for full wave simulation is transferred and achieved successfully, based on a small quantity of full wave simulation data in target domain. Finally, the transfer learning model for far-field prediction is transferred once again for conformal metasurfaces with different structures. Results indicate that the proposed method only consumes 0.1% of full wave simulation time for conformal metasurface far-field calculation. In fewer samples, the model with transfer learning can improve the average accuracy by 19.8%, and the training data account for only 42.9% for the models without transfer learning, which reduces the training data collection time by 50.1%. Moreover, our far-field calculation method demonstrates good transfer performance between conformal metasurfaces with different structures.