Addressing the challenge of low alignment accuracy in 6D position estimation for weakly textured objects, adaptive multimodal-feature fusion is proposed for 6D object position estimation. First, the target object is calibrated using an RGB-D image, and the point cloud obtained from the depth information is segmented using spherical neighborhoods to enhance the ability of capturing detailed information in feature extraction. Second, the involved geometric attributes are strengthened by incorporating new object surface normals to obtain the complementary geometric information of the target. Subsequently, the extracted color, geometric, and normal three-branch features are fused in a high-dimensional space through adaptive feature fusion, enhancing the complementary strengths of each feature. Finally, a regression function is employed to obtain the target pose parameters, employing the predicted pose of high-confidence pixels as an initial estimate. This estimate undergoes iterative optimization to obtain the final pose, realizing accurate 6D pose estimation. Tests are conducted on the LineMOD and YCB-Video datasets, and the experimental results show that the proposed method exhibites significant advantages in object pose estimation accuracy compared to similar methods.