Abstract: The Reynolds number can reach 10^5 in typical reactor core operating conditions, and the coolant flow exhibits significant nonlinearity. An inevitable mismatch between the actual flow boundaries and states and the ideal flow equations can lead to conflicts between the data and the constraints of the governing equations during the solving process. This mutual restriction can cause difficulties in achieving convergence. This paper developed a sparse data-solving method based on deep learning to address this issue. By designing an adaptive mismatch correction scheme, an adaptive adjustment factor is introduced into the governing equations to correct the ideal model dynamically. This approach overcomes the convergence difficulties and accuracy issues caused by the inconsistency between the data and the equations. Based on this technology, the study further explored flow field-solving strategies under small sample data conditions and designed uniform, velocity-gradient-based, and hybrid point distribution strategies. These strategies aim to optimize the spatial distribution of sample points to improve the overall accuracy of the flow field solutions. The results show that the uniform point distribution strategy provides the best optimization effect and significantly enhances the solution accuracy among the three strategies. Even with only 60 small sample points, the proposed method can effectively achieve high-accuracy flow field solutions, providing an efficient and highly applicable solution for solving the flow field of PWR reactor core rod bundles under sparse data conditions.