[30] proposed an error method called the DIDIM method to identify the dynamic parameters of the dynamic equations and the new method was proved to be effective through an experiment with a 2-DOF rigid body robot. The dynamic model is established based on vector mechanics or analytical mechanics [31]. The modeling methods mainly include the Newton-Euler method [32] and the screw method [33], which are based on vector mechanics, as well as the Lagrange method [34], the Hamilton method [35; 36], the Udwadia-Kalaba method [37] and the Birkhoff method [38], which are based on analytical mechanics. There are also some methods that combine the characteristics of vector mechanics and analytical mechanics, such as the Kane method [39]. [PERSON], [PERSON] et al. [40] established the dynamic model of the Gough-Stewart parallel robot by using the Newton-Euler method; [PERSON], [PERSON] et al. [41] established the dynamic model of the Helix robot with the screw method; [PERSON], [PERSON] et al. [42] used the [PERSON] method to establish a dynamic model of a multi-purpose upper limb rehabilitation robot; [PERSON], [PERSON] et al. [43] used [PERSON] method to establish a dynamic model of a rope-net space robot; [PERSON], [PERSON], et al. [44] used the Udwadia-Kalaba method to establish a dynamic model of a modular underwater robot; and [PERSON], [PERSON] et al. [45] used the Kane method to establish a dynamic model of a soft robot. At present, a large number of scholars have conducted in-depth research on the two-dimensional dynamics of industrial robots with few DOFs. However, the research on three-dimensional multi-DOF dynamics is relatively weak and it is very rare to use graph theory to describe industrial robots to establish dynamic models. Compared with the two-dimensional dynamics problem with few DOFs, the three-dimensional multi-DOF dynamics problem has a significant increase in the number of variables and equations, resulting in a very complicated and long expression, which is difficult to calculate and solve. Therefore, the traditional method is no longer applicable [46] and needs to be improved. We consider that the key to solving this problem lies in the expression of the system of dynamic equations formed by the combination of many dynamic equations in the form of a matrix. The matrix expression needs to decouple the dynamic parameters of the rigid bodies that make up the robot system to realize the modularization of the dynamic equations, which is convenient for computer solving and control. We believe that the use of graph theory to describe the robot system can meet the requirements well. This paper selects the most commonly used industrial robot--an industrial robot with six rotational DOFs (6R robot system)--as the research object. The 6R robot system is composed of six rigid bodies and has six rotational DOFs, as shown in Figure 1. In this paper, a three-dimensional dynamic model of the 6R robot system is established based on graph theory. On this basis, a new method of dynamic parameter identification is proposed. A simulation experiment is designed by ADAMS software. The experimental results show that the result of parameter identification is extremely accurate and the dynamic model is accurate enough for the mathematical description of the 6R robot system. This proves the correctness and applicability of the dynamic theory and method used in this article. The 6R robot dynamics modeling theory and method proposed in this paper can realize the accurate 3D dynamic modeling of the 6R robot system. At present, there are very few studies in this area, so our exploratory research is very meaningful. At the same time, we propose for the first time a 6R robot dynamic parameter identification algorithm. The dynamic parameters obtained by this algorithm have extremely high precision, which is a meaningful innovation.