In recent years, the application of scene graphs in image captioning has been increasingly researched. However, the current image captioning models based on scene graphs cannot take into account the previous input retained in long short-term memory (LSTM) networks, which may lead to missed information. In this study, we firstly propose the image captioning network based on original information injection, which keeps the original input information as much as possible and reduces the missed information. Secondly, we consider that the degree of the current graph updating mechanism is too large, which may lead to the missing of node information. Thus, we propose a visit control module to update the weights of visited nodes, avoiding such missing. Finally, we design a graph update factor (GUF) to determine the update level. We conduct experiments on the official dataset: MSCOCO. The mechanism evaluation shows that our model has achieved more competitive results compared with the baseline model.