In this article, a six-step approach is proposed to simulate the grasp and evaluate the grasp quality for a versatile vacuum gripper by tracking the deformation and force-torque wrench of the gripping pad. Over 100 K synthetic grasps are generated for neural network training. Furthermore, a gripping attention convolutional neural network (GA-CNN) is developed to predict the grasp quality for real-world grasp, running by 15 Hz closed-loop control with the real-time robotic observation and force-torque feedback. Various experiments in both the simulation and physical grasps indicate that our GA-CNN can focus on the crucial region of the soft gripping pad to predict grasp qualities and perform a lower average error compared with a same-scale traditional CNN. In addition, the complexity of grasping clutters is defined from Level 1 to Level 9. The proposed grasping method achieves an average success rate of 90.2% for static clutters at Level 1 to Level 8 and an average success rate of >80.0% for dynamic grasping at Level 1 to Level 7, which outperforms state-of-the-art grasping methods.