Aiming at the problem of low accuracy of object grasping position detection in robotic arm grasping detection, a 3D point cloud-based robotic arm grasping position detection method is proposed. Firstly, we design an end-to-end grasping position detection network SE-PointNetGPD (SEPN-GPD for short) based on the attention mechanism, and secondly, to address the problem of redundancy of information in the pointnet network when utilizing the multilayer perceptron MLP with shared weights to process the 3D point cloud data, we introduce the SENet module of the channel attention mechanism, and adaptively adjust the weights of the individual feature regions to improve the feature extraction capability of the network and thus improve the accuracy of the grasping position detection method. The SENet module is introduced to enhance the feature extraction capability of the network by adaptively adjusting the weights of each feature region to improve the accuracy and reliability of grasping position detection, which is then validated on the YCB and BigBIRD public datasets. The experimental results show that the classification accuracies of the SEPN-GPD grasping posture detection method are 86.2% and 85.14%, respectively, and the network has a better model generalization ability and higher robustness and stability, which is better than the current mainstream grasping posture detection methods such as PointNetGPD and GPD.