Recently, many researchers have been attracted in link prediction, which is an effective technique \ used in graph based models analysis. By using the link prediction method the study understands associations between nodes. Most of previous works in this area have not explored the prediction of links in dynamic multi-dimension networks and have not explored the prediction of links which could disappear in the future. This paper argues that these kinds of links are important. At least they can serve as a complement for current link prediction processes in order to plan better for the future. This paper proposes a link prediction model, which is capable of predicting bi-direction links that might exist and may disappear in the future in dynamic multi-dimension networks. Firstly, the study presents the definition of multi-dimensional networks, reduction dimension networks, and dynamic networks. Then paper proposes a forward some algorithms which build multi-dimension networks, reduction dimension networks, and dynamic networks. Next, a give bi-direction link prediction algorithms in dynamic multi-dimension weighted networks. At the end, algorithms above are applied in recommendation networks. Experimental results show that the algorithm can improve the link prediction performance in dynamic multi-dimensional weighted networks.