Robot grasping detection has always been a research focus in the field of robotics, but the robot faces the problem of inaccurate pose estimation when performing multi-object grasping tasks in complex environments. In order to improve this problem, a Transformer based grasping detection model called PTGNet (pyramid Transformer grasp network) was proposed. The PTGNet adopted Transformer modules with pyramid pooling structure and multi-head self-attention mechanism. The pyramid pooling structure could segment and pool feature maps to capture semantic information at different levels and reduce computational complexity, and the multi-head self-attention mechanism effectively extracted global information through powerful feature extraction capabilities, making PTGNet more suitable for visual grasping tasks. In order to verify the performance of the PTGNet, the training and testing for PTGNet were conducted based on different datasets, and the robot arm grasping experiments based on PTGNet were carried out in both simulated and real physical environments. The results showed that the accuracy of PTGNet on Cornell dataset and Jacquard dataset was 98.2% and 94.8%, respectively, showing excellent competitive performance. Compared with other detection models, the PTGNet had excellent generalization ability in multi-target datasets. In the single-object and multi-object grasping experiments conducted in the PyBullet simulation environment, the average grasping success rate of the robot arm reached 98.1% and 96.8%, respectively. In the multi-object grasping experiments conducted in the real physical environment, the average grasping success rate of the robot arm was 93.3%. The experimental results demonstrate the effectiveness and superiority of PTGNet in predicting multi-object grasping pose in complex environment.