The profile of the melt pool is essential in selective laser melting (SLM) to control the process quality and avoid defects. Physics informed neural network (PINN) method is proposed to address challenges in various science and engineering problems when traditional numerical calculations are time-consuming, or deep learning (DL) methods have high demand for data. However, SLM process involves many complex physical phenomena. Low-fidelity data from low-fidelity models struggle to accurately reflect these phenomena, while high-fidelity data from high-fidelity models contains more physical equations, making it difficult for current PINN. This article proposed a transfer learning-enhanced PINN (TLE-PINN) method using high-fidelity data for precise and fast melt pool prediction. It contains the enhanced PINN (EPINN) and transfer learning framework. The EPINN model integrates the heat transfer law and boundary condition to loss function, imposing strong physical constraints on data. Then, the transfer learning framework, combining the concepts of PINN and DL, initially trains with PINN and then further fine-tunes it using DL method. Notably, it only uses a single model, which is more convenient to traditional methods that require two models. The developed solution demonstrates outstanding performance when compared with experiments and existing methods, showing significant potential for industrial applications.