Network performance evaluation is of great significance for network optimization and resource management. However, conventional performance evaluation methods are generally conducted on network areas of cellular scales, which makes it impossible to generate network evaluation results with high geographic precision. To solve this problem and inspired by the research field of computer vision, a deep leaning-based scheme for grid-level performance evaluation of 5G network is proposed and training strategy is designed. First, the grid-level and coarse-grid evaluation modules of the scheme are constructed based on deep learning networks. Secondly, to improve the performance of proposed scheme, a conventional evaluation method is exploited to address the lack of data labels and loss function with regularization term is designed. Moreover, distributed model deployment strategies are provided for the proposed deep learning models. Finally, simulation results illustrate the correlation between generated grid-level evaluation results and input data is strong, which demonstrates the high performance of the proposed scheme.