The invention discloses a visual data completion method based on low-rank tensor ring decomposition and factor prior, and aims to solve the problem that a traditional data completion algorithm based on tensor decomposition depends on initial rank selection, so that a recovery result lacks stability and effectiveness, and a layered tensor decomposition model is designed. Tensor ring decomposition and complementation are realized at the same time, and for the first layer, incomplete tensors are expressed as a series of third-order factors through tensor ring decomposition; for the second layer, the transformation tensor nuclear norm is used for representing the low-rank constraint of the factors, and the degree of freedom of each factor is limited in combination with the factor priori of graph regularization; according to the method, the low-rank structure and the prior information of the factor space are utilized at the same time, on one hand, the model has implicit rank adjustment, the robustness of the model to rank selection can be improved, and therefore the burden of searching the optimal initial rank is relieved, and on the other hand, potential information of tensor data is fully utilized, and the complementation performance is further improved.