In the process of robotic vision grasping， it is difficult for the existing algorithms to perform real-time， accurate and robust pose estimation of the target object under complex background， insufficient illumination， occlusion， etc. Aiming at the above problems， a 6D pose estimation network with fused attention features based on the key point method was proposed. Firstly， Convolutional Block Attention Module （CBAM） was added in the skip connection stage to focus the spatial and channel information， so that the shallow features in the encoding stage were effectively fused with the deep features in the decoding stage， the spatial domain information and accurate position channel information of the feature map were enhanced. Secondly， the attention map of every key point was regressed in a weakly supervised way using a normalized loss function. The attention map was used as the weight of the key point offset at the corresponding pixel position. Finally， the coordinates of keypoints were obtained by accumulating and summing. The experimental results demonstrate that the proposed network reaches 91.3% and 46.3% on the LINEMOD and Occlusion LINEMOD datasets respectively in the ADD（-S） metric. 5.0 percentage points and 5.5 percentage points improvement in the ADD（-S） metric are achieved compared to Pixel Voting Network （PVNet）， which verifies that the proposed network improves the robustness of objects in occlusion scenes.