Grasping the target object is an essential requirement for the robot to provide better services. It becomes complicated especially in cluttered environments, which still remains challenging. This paper proposes a novel grasping chain generation solution that enables the robot to grasp the target after other obstructed objects are moved in a good order. SSD is firstly adopted to acquire the information of detectable objects and then Euclidean clustering is employed to obtain the untrained objects. After that, the minimum bounding box of each object is obtained, which is then projected on the plane and represented by a smooth differentiable minimum ellipse. On this basis, an information density kernel function is designed to express the interaction between objects. By abstracting each ellipse as a node of the graph whose edge weight is calculated by this kernel function, the whole scene is described in a form of graph. To simplify the complexity of the scene graph, we use spectral clustering algorithm to classify the objects, and the taskoriented objects graph is constructed according to the objects closely related to the target one. As a result, the searching space is reduced. With space division of task-oriented objects graph, each candidate grasping chain is iteratively extended by using the heuristic searching and the best chain with the shortest length is determined. The proposed method solves the barrier caused by secondary obstruction and its effectiveness is testified by experiments.