The aim is to accurately recover Internet traffic data, which could reduce the negative impact caused by incomplete data on the network tasks. Since the traffic data could be represented by a fourth order tensor, and considering its spatiotemporal characteristics, an optimal recovery model was proposed based on dthorder tensor singular value decomposition (dthorder TSVD) combined with spatiotemporal regularization strategies. The key feature of this model lies in its ability to deeply explore the data while preserving its internal complex structural properties, thereby achieving higherquality data recovery. An efficient and stable algorithm is developed to solve this model accurately by utilizing the alternating minimization method. Finally, the proposed method was comprehensively validated by simulating both random and structural data loss scenarios on two real internet traffic datasets. Experimental results demonstrate that this method exhibits significant advantages in data recovery performance compared to existing methods.