在有限元分析中，提高网格密度能够显著增强仿真结果的准确性，但同时也需要消耗更多的计算资源，为了解决这一矛盾，通过将Res2Net、U-Net、通道注意力机制、几何特征提取融合在一起，对低网格密度的有限元结果云图数据进行学习，预测高网格密度的有限元结果云图，从而在不牺牲精度的前提下，减少所需的计算成本。模型通过在2倍、4倍和8倍等不同尺度条件下进行实验，在测试数据上的均方误差和平均绝对误差都有显著降低，充分证明了模型在数值预测准确性方面的卓越表现，结果表明，在较少的计算资源投入下，在保证输出结果的高精度下，可利用此模型进行有限元结果云图的加密。

In finite element analysis, increasing mesh density significantly enhances the accuracy of simulation results, but it also demands a greater consumption of computational resources. In order to solve this contradiction, Res2Net, U-Net, channel attention mechanism and geometric feature extraction were integrated to learn the cloud image data of finite element results with low mesh density. Predict high mesh density finite element resulting cloud images, thereby reducing the computational costs required without sacrificing accuracy. By conducting experiments under different scale conditions such as 2 ×, 4 × and 8 ×, the mean square error and mean absolute error of the test data have been significantly reduced, which fully proves the excellent performance of the model in the accuracy of numerical prediction. The results indicate that with minimal computational resource investment and guaranteed high precision of output results, this model can be effectively utilized for the upscaling of finite element result cloud images.