To address the issues of geometric distortion and loss of details in 3D modeling for complex cultural heritage scenes, this paper proposes an improved 3D Gaussian Splatting (3DGS) reconstruction method that integrates LiDAR and illumination-awareness. First, high-precision 3D coordinates from LiDAR point clouds are utilized to guide the initialization of Gaussian Primitives, establishing a precise geometric foundation and effectively overcoming deformation on weakly textured surfaces. Second, an illumination-aware network is constructed to dynamically adjust appearance parameters by combining global illumination from images with LiDAR reflectance intensity. This decouples complex lighting from material properties, accurately reproducing the unique textures of artifacts. Finally, a multi-dimensional joint loss function incorporating photometric, scale, and appearance smoothness constraints is introduced to collaboratively optimize scene geometry, appearance, and camera poses. Experimental results on indoor and outdoor cultural heritage preservation scenarios demonstrate that the proposed method significantly outperforms various comparative algorithms in terms of both visual fidelity and geometric accuracy. The quantitative and qualitative evaluations confirm that our approach effectively eliminates geometric distortions and recovers fine texture details, providing an efficient and reliable technical solution for the digital preservation of cultural heritage.