<p>Physics-informed neural networks (PINNs), as a novel artificial intelligence method for solving partial differential equations, are applicable to solve both forward and inverse problems. This study evaluates the performance of PINNs in solving the temperature diffusion equation of the seawater across six scenarios, including forward and inverse problems under three different boundary conditions. Results demonstrate that PINNs achieved consistently higher accuracy with the Dirichlet and Neumann boundary conditions compared to the Robin boundary condition for both forward and inverse problems. Inaccurate weighting of terms in the loss function can reduce model accuracy. Additionally, the sensitivity of model performance to the positioning of sampling points varied between different boundary conditions. In particular, the model under the Dirichlet boundary condition exhibited superior robustness to variations in point positions during the solutions of inverse problems. In contrast, for the Neumann and Robin boundary conditions, accuracy declines when points were sampled from identical positions or at the same time. Subsequently, the Argo observations were used to reconstruct the vertical diffusion of seawater temperature in the north-central Pacific for the applicability of PINNs in the real ocean. The PINNs successfully captured the vertical diffusion characteristics of seawater temperature, reflected the seasonal changes of vertical temperature under different topographic conditions, and revealed the influence of topography on the temperature diffusion coefficient. The PINNs were proved effective in solving the temperature diffusion equation of seawater with limited data, providing a promising technique for simulating or predicting ocean phenomena using sparse observations.</p>