Low-quality 3D face recognition (FR) is a crucial application in human-computer interaction. However, it is sensitive to changes in 3D shapes and textures caused by time, resulting in poorer recognition performance on the time subset. To solve the problem, we first propose a novel time robust feature extractor (TFE), which introduces an improved Transformer block to mitigate interference from texture and shape changes. Additionally, TFE includes a novel feature mixer (GL-Mixer), which effectively integrates local and global features while reducing redundancy. Finally, we utilize TFE to construct MIHNet, a multi-scale intra-layer fusion network with a hybrid structure for low-quality 3D FR. Experiments on two publicly available low-quality datasets and one cross-quality dataset demonstrate that MIHNet achieves competitive recognition accuracy, particularly attaining state-of-the-art (SOTA) performance on the time subset.