In this paper, deep learning, 3D scene modeling, and resource sharing platform are used to solve the protection and inheritance challenges faced by red cultural resources due to age erosion. Firstly, a triple-domain transformation network based on variational autoencoder (VAE) is introduced to realize historical photo restoration. The point cloud simplification algorithm with limited normal precision is adopted in the 3D reconstruction, and the point cloud data is woven into a 3D model by the partitioning algorithm. Finally, based on the resource management strategy of “distributed storage, centralized management”, the physical resources are distributed and the key index information is centrally managed to achieve standardized resource sharing. The photo restoration model based on VAE has PSNR=38.98 and SSIM=0.912 in the red cultural history RealPhoto dataset, which is 6.24% and 6.17% higher than the second place TDT model. In the subjective evaluation, more than 82.96% of users rated the VAE restoration effect as the first place. For 3D reconstruction, the partitioning algorithm leads the average performance on the ShapeNet dataset across the board, with a CD value and F1 score of 0.32 and 83.91, respectively, which are both better than the point-by-point insertion method and the triangular mesh growth method. In the resource sharing efficiency test, even if the amount of resources increases to 5000, the uploading efficiency of the hybrid model in this paper is still stabilized at a high level of 93.4%, and in the anti-jamming test, its network request acceptance rate is kept at 100%.
