Aiming at the unstructured grasping environment in real life, an autonomous grasping method based on the basic objects composition of irregular objects is proposed. The key to autonomous grasping of robots lies not only in the recognition of object types, but also in the good grasping of object's shapes (such as the composition of shape primitives). The irregular complex object is simplified as the composition of some simple objects. The 3D data points of the grasped object are segmented into the main body and the branch part based on the mesh segmentation using feature point and core extraction (MFC) algorithm, and the parts are fitted to a sphere, an ellipsoid, a cylinder or a parallelepiped according to an optimal fitting algorithm. Based on the simplified result, the grasping posture is constrained, and then the grasping training of the simplified object is completed to obtain the optimal grasping frame, thereby realizing the autonomous grasping of the unknown object. Finally, the method proposed realizes the grasping operation with an accuracy of 93.3% on the Baxter robot. The experimental results show that the method can be applied to unknown irregular objects of different shapes and postures with strong robustness.