To address the problems of low accuracy and time consuming detection of unknown object grasping pose in the robotic arm grasping detection task, a multi-modal feature grasping pose detection network with attention mechanism is proposed. Firstly, a multi-modal feature fusion module is designed to fuse the multi-modal features and enhance their weighting. Then, to address the problem that the shallow residual network is weak in extracting key features, a convolutional attention module is introduced to further improve the feature extraction ability of the network. Finally, the optimal grasp detection pose is obtained by direct regression fitting of the extracted features through the fully connected layer. The experimental results show that the detection accuracy of image splitting and object splitting on the Cornell grasp dataset is 98.9% and 98.7% respectively, and the detection speed is 51FPS. The success rate is 95% for 100 real-world grabs of 10 types of objects.